gpu_model_runner.py 232 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import itertools
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import time
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from collections import defaultdict
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from collections.abc import Iterator, Sequence
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from contextlib import contextmanager
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from copy import copy, deepcopy
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from functools import reduce
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from itertools import product
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionMetadata,
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    AttentionType,
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    MultipleOf,
)
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from vllm.attention.layer import Attention, MLAAttention
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphStat, CUDAGraphWrapper
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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,
    XDRotaryEmbedding,
)
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (
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    SupportsMRoPE,
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    SupportsMultiModal,
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    SupportsXDRoPE,
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    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
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    supports_xdrope,
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)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    get_dcp_local_seq_lens,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
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    ECConnectorOutput,
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    KVConnectorOutput,
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    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
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    make_empty_encoder_model_runner_output,
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)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.logits_processor.interface import LogitsProcessor
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_utils import (
    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: LogprobsTensors | None,
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        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
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        vocab_size: int,
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    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

        # Event on the copy stream so we can synchronize the non-blocking copy.
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        self.async_copy_ready_event = torch.Event()
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        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids
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        self.vocab_size = vocab_size
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        self._logprobs_tensors = logprobs_tensors
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        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
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            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
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                "cpu", non_blocking=True
            )
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            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
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            self.async_copy_ready_event.record()
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    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
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        This function blocks until the copy is finished.
        """
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        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
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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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        if max_gen_len == 1:
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            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
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            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
            cu_num_tokens = None
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        else:
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            valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
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                self.sampled_token_ids_cpu,
                self.vocab_size,
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                self._invalid_req_indices,
                return_cu_num_tokens=self._logprobs_tensors_cpu is not None,
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            )
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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:
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            output.logprobs = self._logprobs_tensors_cpu.tolists(cu_num_tokens)
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        return output


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

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
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    ec_connector_output: ECConnectorOutput | None
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    cudagraph_stats: CUDAGraphStat | 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.inputs_embeds_size = model_config.get_inputs_embeds_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.uses_xdrope_dim = model_config.uses_xdrope_dim
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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        # Lazy initializations
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        # self.model: nn.Module  # Set after load_model
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        # Initialize in initialize_kv_cache
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        self.kv_caches: list[torch.Tensor] = []
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        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
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        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
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        # self.kv_cache_config: KVCacheConfig

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
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            self.drafter: (
                NgramProposer | SuffixDecodingProposer | EagleProposer | MedusaProposer
            )
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            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
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            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
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            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)
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                if self.speculative_config.method == "eagle3":
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                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
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            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
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                )
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler(self.sampler)
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        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens

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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
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        self.prepare_inputs_event: torch.Event | None = None
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        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
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            self.prepare_inputs_event = torch.Event()
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        # self.cudagraph_batch_sizes sorts in ascending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
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            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        self.encoder_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(
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            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
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        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
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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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        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            # Similar to mrope but use assigned dimension number for RoPE, 4 as default.
            self.xdrope_positions = self._make_buffer(
                (self.uses_xdrope_dim, 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,
        )
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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

        self.kv_sharing_fast_prefill_logits_indices = None
        if self.cache_config.kv_sharing_fast_prefill:
            self.kv_sharing_fast_prefill_logits_indices = torch.zeros(
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                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
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        self.uniform_decode_query_len = 1 + self.num_spec_tokens
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        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
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        self.reorder_batch_threshold: int | None = None
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        # Attention layers that are only in the KVCacheConfig of the runner
        # (e.g., KV sharing, encoder-only attention), but not in the
        # KVCacheConfig of the scheduler.
        self.runner_only_attn_layers: set[str] = set()

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        # Cached outputs.
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        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
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        self.transfer_event = torch.Event()
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        self.sampled_token_ids_pinned_cpu = torch.empty(
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            (self.max_num_reqs, 1),
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            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
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        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
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        self.valid_sampled_token_count_event: torch.Event | None = None
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        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
        if self.use_async_scheduling and self.num_spec_tokens:
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            self.valid_sampled_token_count_event = torch.Event()
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            self.valid_sampled_token_count_copy_stream = torch.cuda.Stream()
        self.valid_sampled_token_count_cpu = torch.empty(
            self.max_num_reqs,
            dtype=torch.int64,
            device="cpu",
            pin_memory=self.pin_memory,
        )

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        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
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        self.kv_connector_output: KVConnectorOutput | None = None
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    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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    @torch.inference_mode()
    def init_fp8_kv_scales(self) -> None:
        """
        Re-initialize the KV cache and FP8 scales after waking from sleep.
        1. Zero out the KV cache tensors to remove garbage data from re-allocation.
        2. Reset Attention layer scaling factors (_k_scale, _v_scale) to 1.0.
          If these are left at 0.0 (default after wake_up), all KV cache values
          become effectively zero, causing gibberish output.
        """
        if not self.cache_config.cache_dtype.startswith("fp8"):
            return

        kv_caches = getattr(self, "kv_caches", [])
        for cache_tensor in kv_caches:
            if cache_tensor is not None:
                cache_tensor.zero_()

        k_attr_names = ("_k_scale", "k_scale")
        v_attr_names = ("_v_scale", "v_scale")

        attn_layers = self.compilation_config.static_forward_context
        for name, module in attn_layers.items():
            if isinstance(module, (Attention, MLAAttention)):
                # TODO: Generally, scale is 1.0 if user uses on-the-fly fp8
                # kvcache quant. However, to get better accuracy, compression
                # frameworks like llm-compressors allow users to tune the
                # scale. We may need to restore the specific calibrated scales
                # here in the future.
                k_scale_val, v_scale_val = 1.0, 1.0

                # Processing K Scale
                for attr in k_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(k_scale_val)

                # Processing V Scale
                for attr in v_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(v_scale_val)

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    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
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            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
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            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
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            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
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            return self.positions.gpu[num_tokens]

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    def _make_buffer(
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        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
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    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

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        if not self.is_pooling_model:
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            return model_kwargs

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

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

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

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

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

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

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        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
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                decode_threshold=self.reorder_batch_threshold,
            )
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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
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        """Initialize attributes from torch.cuda.get_device_properties"""
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        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

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

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    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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        """Update the cached states and the persistent batch with the scheduler
        output.

        The updated states are used by the `_prepare_inputs` function to create
        the input GPU tensors for the model.

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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
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        """
        # Remove finished requests from the cached states.
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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            self.num_prompt_logprobs.pop(req_id, None)
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
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            self.input_batch.remove_request(req_id)
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        # Free the cached encoder outputs.
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        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
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        resumed_req_ids = scheduler_output.scheduled_cached_reqs.resumed_req_ids
        # NOTE(zhuohan): cached_req_ids and resumed_req_ids are usually disjoint,
        # so `(scheduled_req_ids - resumed_req_ids) == scheduled_req_ids` holds
        # apart from the forced-preemption case in reset_prefix_cache. And in
        # that case we include the resumed_req_ids in the unscheduled set so
        # that they get cleared from the persistent batch before being re-scheduled
        # in the normal resumed request path.
        unscheduled_req_ids = cached_req_ids - (scheduled_req_ids - resumed_req_ids)
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        # 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:
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            self.input_batch.remove_request(req_id)
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        reqs_to_add: list[CachedRequestState] = []
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        # Add new requests to the cached states.
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        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
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            pooling_params = new_req_data.pooling_params
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            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
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                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

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            if self.is_pooling_model:
                assert pooling_params is not None
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                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
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                model = cast(VllmModelForPooling, self.get_model())
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                to_update = model.pooler.get_pooling_updates(task)
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                to_update.apply(pooling_params)

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            req_state = CachedRequestState(
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                req_id=req_id,
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                prompt_token_ids=new_req_data.prompt_token_ids,
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                prompt_embeds=new_req_data.prompt_embeds,
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                mm_features=new_req_data.mm_features,
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                sampling_params=sampling_params,
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                pooling_params=pooling_params,
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                generator=generator,
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
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                output_token_ids=[],
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                lora_request=new_req_data.lora_request,
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            )
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            self.requests[req_id] = req_state

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            if sampling_params and sampling_params.prompt_logprobs is not None:
                self.num_prompt_logprobs[req_id] = (
                    self.input_batch.vocab_size
                    if sampling_params.prompt_logprobs == -1
                    else sampling_params.prompt_logprobs
                )

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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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                self._init_mrope_positions(req_state)
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            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

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            reqs_to_add.append(req_state)
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        # Update the states of the running/resumed requests.
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        is_last_rank = get_pp_group().is_last_rank
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        req_data = scheduler_output.scheduled_cached_reqs
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        # Wait until valid_sampled_tokens_count is copied to cpu,
        # then use it to update actual num_computed_tokens of each request.
        valid_sampled_token_count = self._get_valid_sampled_token_count()

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        for i, req_id in enumerate(req_data.req_ids):
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
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            resumed_from_preemption = req_id in req_data.resumed_req_ids
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            num_output_tokens = req_data.num_output_tokens[i]
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            req_index = self.input_batch.req_id_to_index.get(req_id)
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            # prev_num_draft_len is used in async scheduling mode with
            # spec decode. it indicates if need to update num_computed_tokens
            # of the request. for example:
            # fist step: num_computed_tokens = 0, spec_tokens = [],
            # prev_num_draft_len = 0.
            # second step: num_computed_tokens = 100(prompt lenth),
            # spec_tokens = [a,b], prev_num_draft_len = 0.
            # third step: num_computed_tokens = 100 + 2, spec_tokens = [c,d],
            # prev_num_draft_len = 2.
            # num_computed_tokens in first step and second step does't contain
            # the spec tokens length, but in third step it contains the
            # spec tokens length. we only need to update num_computed_tokens
            # when prev_num_draft_len > 0.
            if req_state.prev_num_draft_len:
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
                    assert self.input_batch.prev_req_id_to_index is not None
                    prev_req_index = self.input_batch.prev_req_id_to_index[req_id]
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    num_rejected = req_state.prev_num_draft_len - num_accepted
                    num_computed_tokens -= num_rejected
                    req_state.output_token_ids.extend([-1] * num_accepted)
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            # Update the cached states.
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            req_state.num_computed_tokens = num_computed_tokens
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            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
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                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
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                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
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                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
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            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
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                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
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                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
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            # Update the block IDs.
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            if not resumed_from_preemption:
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                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
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                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
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                        block_ids.extend(new_ids)
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            else:
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                assert req_index is None
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                assert new_block_ids is not None
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                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
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                req_state.block_ids = new_block_ids
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            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.
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                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:]

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                reqs_to_add.append(req_state)
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                continue

            # Update the persistent batch.
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            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
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            if new_block_ids is not None:
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                self.input_batch.block_table.append_row(new_block_ids, req_index)
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            # 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)
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                self.input_batch.token_ids_cpu[
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                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
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                self.input_batch.num_tokens[req_index] = end_token_index
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            # Add spec_token_ids to token_ids_cpu.
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            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
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                req_id, []
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            )
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            num_spec_tokens = len(spec_token_ids)
            # For async scheduling, token_ids_cpu assigned from
            # spec_token_ids are placeholders and will be overwritten in
            # _prepare_input_ids.
            if num_spec_tokens:
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                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[
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                    req_index, start_index:end_token_index
                ] = spec_token_ids
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                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
980
981
982
983
984
985

            # 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.
986
987
            self.input_batch.spec_token_ids[req_index].clear()
            self.input_batch.spec_token_ids[req_index].extend(spec_token_ids)
988

989
990
991
992
993
994
995
996
997
            # there are no draft tokens with async scheduling,
            # we clear the spec_decoding info in scheduler_output and
            # use normal sampling but rejection_sampling.
            if self.use_async_scheduling:
                req_state.prev_num_draft_len = num_spec_tokens
                if num_spec_tokens and self._draft_token_ids is None:
                    scheduler_output.total_num_scheduled_tokens -= num_spec_tokens
                    scheduler_output.num_scheduled_tokens[req_id] -= num_spec_tokens
                    scheduler_output.scheduled_spec_decode_tokens.pop(req_id, None)
998
999
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1000
1001
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1002

1003
1004
1005
1006
1007
1008
        # 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()
1009

1010
    def _update_states_after_model_execute(
1011
1012
        self, output_token_ids: torch.Tensor
    ) -> None:
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
        """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.
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
        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()
        )
1045
1046
1047
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

1048
    def _init_mrope_positions(self, req_state: CachedRequestState):
1049
1050
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1051
1052
1053
1054
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1055
1056

        req_state.mrope_positions, req_state.mrope_position_delta = (
1057
            mrope_model.get_mrope_input_positions(
1058
                req_state.prompt_token_ids,
1059
                req_state.mm_features,
1060
            )
1061
        )
1062

1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
    def _init_xdrope_positions(self, req_state: CachedRequestState):
        model = self.get_model()
        xdrope_model = cast(SupportsXDRoPE, model)
        assert req_state.prompt_token_ids is not None, (
            "XD-RoPE requires prompt_token_ids to be available."
        )
        assert supports_xdrope(model), "XD-RoPE support is not implemented."

        req_state.xdrope_positions = xdrope_model.get_xdrope_input_positions(
            req_state.prompt_token_ids,
            req_state.mm_features,
        )

1076
    def _extract_mm_kwargs(
1077
        self,
1078
1079
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1080
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1081
            return {}
1082

1083
1084
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
1085
1086
1087
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
1088

1089
        # Input all modalities at once
1090
        model = cast(SupportsMultiModal, self.model)
1091
1092
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1093
1094
1095
1096
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1097
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1098
1099
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1100

1101
        return mm_kwargs_combined
1102

1103
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1104
        if not self.is_multimodal_raw_input_only_model:
1105
            return {}
1106

1107
1108
1109
1110
1111
        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)
1112

1113
1114
1115
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1116
        cumsum_dtype: np.dtype | None = None,
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
    ) -> 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

1133
    def _prepare_input_ids(
1134
1135
1136
1137
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1138
    ) -> None:
1139
        """Prepare the input IDs for the current batch.
1140

1141
1142
1143
1144
1145
1146
1147
        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)
1148
1149
1150
            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)
1151
1152
1153
1154
1155
1156
1157
            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
1158
1159
1160
1161
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1162
1163
        indices_match = True
        max_flattened_index = -1
1164
1165
1166
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1167
1168
1169
1170
1171
        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.
1172
1173
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1174
                flattened_index = cu_num_tokens[cur_index].item() - 1
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
                # example: cu_num_tokens = [2, 5, 8], draft_tokens = [1, 2, 2]
                # sample_flattened_indices = [0, 2, 5]
                # spec_flattened_indices = [1,   3, 4,    6, 7]
                sample_flattened_indices.append(flattened_index - draft_len)
                spec_flattened_indices.extend(
                    range(flattened_index - draft_len + 1, flattened_index + 1)
                )
                start = prev_index * self.num_spec_tokens
                # prev_draft_token_indices is used to find which draft_tokens_id
                # should be copied to input_ids
                # example: prev draft_tokens_id [[1,2], [3,4], [5, 6]]
                # flatten draft_tokens_id [1,2,3,4,5,6]
                # draft_len of each request [1, 2, 1]
                # then prev_draft_token_indices is [0,   2, 3,   4]
                prev_draft_token_indices.extend(range(start, start + draft_len))
1190
                indices_match &= prev_index == flattened_index
1191
                max_flattened_index = max(max_flattened_index, flattened_index)
1192
1193
1194
        num_commmon_tokens = len(sample_flattened_indices)
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
        if num_commmon_tokens < total_without_spec:
1195
1196
1197
            # 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)
1198
1199
1200
            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)
1201
1202
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1203
            # So input_ids.cpu will have all the input ids.
1204
1205
1206
1207
1208
1209
1210
            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_(
1211
1212
1213
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1214
1215
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1216
            return
1217
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1218
1219
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1220
        ).to(self.device, non_blocking=True)
1221
        prev_common_req_indices_tensor = torch.tensor(
1222
1223
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1224
1225
        self.input_ids.gpu.scatter_(
            dim=0,
1226
            index=sampled_tokens_index_tensor,
1227
            src=self.input_batch.prev_sampled_token_ids[
1228
1229
1230
                prev_common_req_indices_tensor, 0
            ],
        )
1231

1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
        # Scatter the draft tokens after the sampled tokens are scattered.
        if self._draft_token_ids is None or not spec_flattened_indices:
            return

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

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

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

1255
1256
    def _get_encoder_seq_lens(
        self,
1257
        num_scheduled_tokens: dict[str, int],
1258
1259
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1260
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1261
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1262
            return None, None
1263
1264
1265

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1266
        for req_id in num_scheduled_tokens:
1267
            req_index = self.input_batch.req_id_to_index[req_id]
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
            req_state = self.requests[req_id]
            if req_state.mm_features is None:
                self.encoder_seq_lens.np[req_index] = 0
                continue

            # Get the total number of encoder input tokens for running encoder requests
            # whether encoding is finished or not so that cross-attention knows how
            # many encoder tokens to attend to.
            encoder_input_tokens = sum(
                feature.mm_position.length for feature in req_state.mm_features
            )
            self.encoder_seq_lens.np[req_index] = encoder_input_tokens

        self.encoder_seq_lens.copy_to_gpu(num_reqs)
        encoder_seq_lens = self.encoder_seq_lens.gpu[:num_reqs]
        encoder_seq_lens_cpu = self.encoder_seq_lens.np[:num_reqs]
1284

1285
        return encoder_seq_lens, encoder_seq_lens_cpu
1286

1287
    def _prepare_inputs(
1288
1289
1290
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1291
1292
    ) -> tuple[
        torch.Tensor,
1293
        SpecDecodeMetadata | None,
1294
    ]:
1295
1296
        """
        :return: tuple[
1297
            logits_indices, spec_decode_metadata,
1298
1299
        ]
        """
1300
1301
1302
1303
1304
1305
1306
        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.
1307
        self.input_batch.block_table.commit_block_table(num_reqs)
1308
1309
1310

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1311
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1312

1313
1314
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1315
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1316
1317

        # Get positions.
1318
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1319
1320
1321
1322
1323
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1324

1325
1326
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1327
        if self.uses_mrope:
1328
1329
            self._calc_mrope_positions(scheduler_output)

1330
1331
1332
1333
1334
        # Calculate XD-RoPE positions.
        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            self._calc_xdrope_positions(scheduler_output)

1335
1336
1337
1338
        # 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.
1339
1340
1341
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1342
        token_indices_tensor = torch.from_numpy(token_indices)
1343

1344
1345
1346
        # 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.
1347
1348
1349
1350
1351
1352
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1353
        if self.enable_prompt_embeds:
1354
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1355
1356
1357
1358
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1359
1360
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393

        # 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:
1394
1395
1396
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1397
1398

                output_idx += num_sched
1399

1400
1401
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1402
1403

        # Prepare the attention metadata.
1404
        self.query_start_loc.np[0] = 0
1405
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1406
1407
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1408
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1409
        self.query_start_loc.copy_to_gpu()
1410
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1411

1412
        self.seq_lens.np[:num_reqs] = (
1413
1414
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1415
        # Fill unused with 0 for full cuda graph mode.
1416
1417
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1418

1419
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1420
1421
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1422
        # Record which requests should not be sampled,
1423
        # so that we could clear the sampled tokens before returning
1424
1425
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1426
        )
1427
        self.discard_request_mask.copy_to_gpu(num_reqs)
1428

1429
        # Copy the tensors to the GPU.
1430
1431
1432
1433
1434
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1435

1436
        if self.uses_mrope:
1437
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1438
1439
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1440
1441
                non_blocking=True,
            )
1442
1443
1444
1445
1446
1447
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
                non_blocking=True,
            )
1448
1449
        else:
            # Common case (1D positions)
1450
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1451

1452
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1453
1454
1455
1456
1457
1458
1459
        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
1460
            num_draft_tokens = None
1461
            spec_decode_metadata = None
1462
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1463
1464
1465
1466
1467
        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)
1468
1469
1470
            # 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)
1471
1472
1473
1474
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1475
1476
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1477
1478
1479
1480
1481
1482
1483
1484
                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
                )
1485
            spec_decode_metadata = self._calc_spec_decode_metadata(
1486
1487
                num_draft_tokens, cu_num_tokens
            )
1488
            logits_indices = spec_decode_metadata.logits_indices
1489
            num_sampled_tokens = num_draft_tokens + 1
1490
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1491
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1492
1493
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1494

1495
1496
1497
1498
1499
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1500
            )
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1512
        num_tokens: int,
1513
        num_reqs: int,
1514
1515
1516
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1517
1518
1519
1520
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1521
        num_scheduled_tokens: dict[str, int] | None = None,
1522
1523
1524
1525
1526
        cascade_attn_prefix_lens: list[list[int]] | None = None,
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1527
1528
1529
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs

1530
        logits_indices_padded = None
1531
        num_logits_indices = None
1532
1533
1534
1535
1536
1537
        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
                )
1538

1539
1540
1541
1542
1543
1544
        # 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,
1545
                self.parallel_config.cp_kv_cache_interleave_size,
1546
            )
1547
1548
            self.dcp_local_seq_lens.cpu[num_reqs:].fill_(0)
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs_padded)
1549

1550
1551
1552
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1553

1554
1555
1556
1557
1558
1559
1560
1561
        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()

1562
1563
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1564
1565
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1566
1567
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1568

1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
        # Used in the below loop, uses padded shapes
        query_start_loc = self.query_start_loc.gpu[: num_reqs_padded + 1]
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs_padded + 1]
        seq_lens = self.seq_lens.gpu[:num_reqs_padded]
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs_padded]
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs_padded
        ]

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

        spec_decode_common_attn_metadata = None

1585
1586
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1587
        for kv_cache_gid, kv_cache_group in enumerate(
1588
1589
            self.kv_cache_config.kv_cache_groups
        ):
1590
1591
            encoder_seq_lens, encoder_seq_lens_cpu = self._get_encoder_seq_lens(
                num_scheduled_tokens or {},
1592
                kv_cache_group.kv_cache_spec,
1593
                num_reqs_padded,
1594
            )
1595

1596
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1597
1598
1599
                # 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(
1600
                    (num_reqs_padded, 1),
1601
                    dtype=torch.int32,
1602
1603
1604
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1605
                    (num_tokens_padded,),
1606
1607
1608
                    dtype=torch.int64,
                    device=self.device,
                )
1609
            else:
1610
                blk_table = self.input_batch.block_table[kv_cache_gid]
1611
1612
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
                slot_mapping = blk_table.slot_mapping.gpu[:num_tokens_padded]
1613
1614

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
1615
1616
1617
                # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
                slot_mapping[num_tokens:num_tokens_padded].fill_(-1)
                blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
1618

1619
            common_attn_metadata = CommonAttentionMetadata(
1620
1621
1622
1623
1624
                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,
1625
1626
1627
                num_actual_tokens=num_tokens_padded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
1628
                max_seq_len=max_seq_len,
1629
1630
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1631
                logits_indices_padded=logits_indices_padded,
1632
                num_logits_indices=num_logits_indices,
1633
                causal=True,
1634
                encoder_seq_lens=encoder_seq_lens,
1635
                encoder_seq_lens_cpu=encoder_seq_lens_cpu,
1636
                dcp_local_seq_lens=dcp_local_seq_lens,
1637
                dcp_local_seq_lens_cpu=dcp_local_seq_lens_cpu,
1638
1639
            )

1640
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1641
                if isinstance(self.drafter, EagleProposer):
1642
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1643
1644
1645
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1646

1647
1648
1649
1650
1651
1652
            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
                )
1653
                builder = attn_group.get_metadata_builder()
1654

1655
                extra_attn_metadata_args = {}
1656
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1657
                    extra_attn_metadata_args = dict(
1658
1659
1660
                        num_accepted_tokens=self.num_accepted_tokens.gpu[
                            :num_reqs_padded
                        ],
1661
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
1662
                            :num_reqs_padded
1663
                        ],
1664
1665
                    )

1666
1667
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1668
1669
                        ubatch_slices, common_attn_metadata
                    )
1670
                    for ubid, common_attn_metadata in enumerate(
1671
1672
                        common_attn_metadata_list
                    ):
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
                        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:
1684
1685
1686
1687
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
                    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,
                        )
1698
1699
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1700

1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
        if spec_decode_common_attn_metadata is not None and (
            num_reqs != num_reqs_padded or num_tokens != num_tokens_padded
        ):
            # Currently the drafter still only uses piecewise cudagraphs (and modifies
            # the attention metadata in directly), and therefore does not want to use
            # padded attention metadata.
            spec_decode_common_attn_metadata = (
                spec_decode_common_attn_metadata.unpadded(num_tokens, num_reqs)
            )

1711
        return attn_metadata, spec_decode_common_attn_metadata
1712

1713
1714
1715
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
1716
        num_computed_tokens: np.ndarray,
1717
1718
1719
1720
1721
1722
1723
        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
        """
1724

1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
        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,
1739
                        num_computed_tokens,
1740
1741
1742
1743
1744
1745
1746
1747
                        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
1748

1749
1750
1751
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
1752
        num_computed_tokens: np.ndarray,
1753
        num_common_prefix_blocks: int,
1754
1755
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
    ) -> 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.
        """
1774

1775
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
        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]
1813
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1814
1815
1816
1817
1818
        # 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.
1819
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
1820
        # common_prefix_len should be a multiple of the block size.
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
        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
        )
1832
1833
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1834
1835
1836
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1837
            num_kv_heads=kv_cache_spec.num_kv_heads,
1838
            use_alibi=self.use_alibi,
1839
            use_sliding_window=use_sliding_window,
1840
            use_local_attention=use_local_attention,
1841
            num_sms=self.num_sms,
1842
            dcp_world_size=self.dcp_world_size,
1843
1844
1845
        )
        return common_prefix_len if use_cascade else 0

1846
1847
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1848
        for index, req_id in enumerate(self.input_batch.req_ids):
1849
1850
1851
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1852
1853
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1854
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1855
1856
                req.prompt_token_ids, req.prompt_embeds
            )
1857
1858

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1859
1860
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
            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

1874
1875
1876
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1877
1878
1879
1880
1881
1882
1883
                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

1884
                assert req.mrope_position_delta is not None
1885
                MRotaryEmbedding.get_next_input_positions_tensor(
1886
                    out=self.mrope_positions.np,
1887
1888
1889
1890
1891
                    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,
                )
1892
1893
1894

                mrope_pos_ptr += completion_part_len

1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
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1922
1923
1924
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1926
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1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
    def _calc_xdrope_positions(self, scheduler_output: "SchedulerOutput"):
        xdrope_pos_ptr = 0
        for index, req_id in enumerate(self.input_batch.req_ids):
            req = self.requests[req_id]
            assert req.xdrope_positions is not None

            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds
            )

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
            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 xdrope_positions are pre-computed
                dst_start = xdrope_pos_ptr
                dst_end = xdrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

                self.xdrope_positions.cpu[:, dst_start:dst_end] = req.xdrope_positions[
                    :, src_start:src_end
                ]
                xdrope_pos_ptr += prompt_part_len

            if completion_part_len > 0:
                # compute completion's xdrope_positions on-the-fly
                dst_start = xdrope_pos_ptr
                dst_end = xdrope_pos_ptr + completion_part_len

                XDRotaryEmbedding.get_next_input_positions_tensor(
                    out=self.xdrope_positions.np,
                    out_offset=dst_start,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )

                xdrope_pos_ptr += completion_part_len

1942
1943
    def _calc_spec_decode_metadata(
        self,
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
        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
1960
1961
1962
1963

        # 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(
1964
1965
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1966
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1967
        logits_indices = np.repeat(
1968
1969
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1970
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1971
1972
1973
1974
1975
1976
        logits_indices += arange

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

        # Compute the draft logits indices.
1977
1978
1979
        # 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(
1980
1981
            num_draft_tokens, cumsum_dtype=np.int32
        )
1982
1983
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1984
1985
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1986
1987
1988
1989
1990
        # [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(
1991
1992
            self.device, non_blocking=True
        )
1993
1994
1995
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1996
1997
1998
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1999
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
2000
2001
            self.device, non_blocking=True
        )
2002
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2003
2004
            self.device, non_blocking=True
        )
2005

2006
2007
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2008
        draft_token_ids = self.input_ids.gpu[logits_indices]
2009
2010
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2011
        return SpecDecodeMetadata(
2012
2013
2014
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2015
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2016
2017
2018
2019
2020
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2021
2022
2023
2024
2025
2026
2027
    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
2028
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2029
2030
2031
2032
2033
        # 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_(
2034
2035
2036
2037
2038
2039
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
2040
2041
2042
2043
2044
            # 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
2045
2046
2047
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2048
2049
        return logits_indices_padded

2050
2051
2052
2053
2054
2055
2056
2057
    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
2058
                inputs.
2059
2060
2061
2062
2063
2064

        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
        """
2065
2066
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2067
            return [], []
2068
        # Batch the multi-modal inputs.
2069
        mm_kwargs = list[MultiModalKwargsItem]()
2070
2071
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
2072
2073
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2074
2075

            for mm_input_id in encoder_input_ids:
2076
                mm_feature = req_state.mm_features[mm_input_id]
2077
2078
                if mm_feature.data is None:
                    continue
2079
2080
2081
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
2082

2083
2084
        return mm_kwargs, mm_hashes_pos

2085
2086
2087
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2088
2089
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
2090
2091
            scheduler_output
        )
2092
2093

        if not mm_kwargs:
2094
            return []
2095

2096
2097
2098
2099
2100
2101
2102
        # 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.
2103
        model = cast(SupportsMultiModal, self.model)
2104
        encoder_outputs: list[torch.Tensor] = []
2105
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
2106
2107
2108
2109
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
2110
            multimodal_cpu_fields=model.multimodal_cpu_fields,
2111
        ):
2112
            curr_group_outputs: list[torch.Tensor] = []
2113
2114

            # EVS-related change.
2115
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2116
            # processing multimodal data. This solves the issue with scheduler
2117
2118
2119
2120
            # 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)
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
            # 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,
2137
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
2138
                        )
2139
                    )
2140

2141
                    micro_batch_outputs = model.embed_multimodal(
2142
2143
                        **micro_batch_mm_inputs
                    )
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153

                    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.
2154
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)  # type: ignore[assignment]
2155

2156
2157
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
2158
                expected_num_items=num_items,
2159
            )
2160
            encoder_outputs.extend(curr_group_outputs)
2161

2162
2163
2164
        # 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(
2165
2166
2167
                output,
                is_embed=pos_info.is_embed,
            )
2168
2169
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2170

2171
2172
        return encoder_outputs

2173
    def _gather_mm_embeddings(
2174
2175
        self,
        scheduler_output: "SchedulerOutput",
2176
        shift_computed_tokens: int = 0,
2177
2178
2179
2180
2181
2182
2183
2184
    ) -> 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
2185
        should_sync_mrope_positions = False
2186
        should_sync_xdrope_positions = False
2187

2188
        for req_id in self.input_batch.req_ids:
2189
2190
            mm_embeds_req: list[torch.Tensor] = []

2191
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2192
            req_state = self.requests[req_id]
2193
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2194

2195
2196
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2197
2198
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214

                # 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,
2215
2216
                    num_encoder_tokens,
                )
2217
                assert start_idx < end_idx
2218

2219
                mm_hash = mm_feature.identifier
2220
                encoder_output = self.encoder_cache.get(mm_hash, None)
2221
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2222
2223
2224
2225

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

2226
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2227
2228
2229
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
2230

2231
2232
2233
2234
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
2235
2236
2237
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2238
                assert req_state.mrope_positions is not None
2239
2240
2241
2242
2243
2244
2245
                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,
2246
2247
                    )
                )
2248
2249
2250
2251
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2252
2253
2254
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
2255
2256
2257

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2258
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2259

2260
2261
2262
2263
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

2264
        return mm_embeds, is_mm_embed
2265

2266
    def get_model(self) -> nn.Module:
2267
        # get raw model out of the cudagraph wrapper.
2268
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2269
            return self.model.unwrap()
2270
2271
        return self.model

2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
    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

2287
2288
2289
2290
2291
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2292
2293
        supported_tasks = list(model.pooler.get_supported_tasks())

2294
        if self.scheduler_config.enable_chunked_prefill:
2295
2296
2297
2298
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2299

2300
2301
            logger.debug_once(
                "Chunked prefill is not supported with "
2302
2303
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2304
2305
2306
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2307
2308
2309
2310
2311

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2312
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2313
2314

        return supported_tasks
2315

2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
    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)

2326
    def sync_and_slice_intermediate_tensors(
2327
2328
        self,
        num_tokens: int,
2329
        intermediate_tensors: IntermediateTensors | None,
2330
2331
        sync_self: bool,
    ) -> IntermediateTensors:
2332
2333
2334
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2335
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2336
2337
2338
2339
2340
2341

        # 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():
2342
                is_scattered = k == "residual" and is_rs
2343
                copy_len = num_tokens // tp if is_scattered else num_tokens
2344
                self.intermediate_tensors[k][:copy_len].copy_(
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
                    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:
2358
2359
2360
2361
2362
2363
2364
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2365
2366
        model = self.get_model()
        assert is_mixture_of_experts(model)
2367
2368
2369
        self.eplb_state.step(
            is_dummy,
            is_profile,
2370
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2371
2372
        )

2373
2374
2375
2376
2377
2378
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2379
2380
2381
        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"
        )
2382

2383
        hidden_states = hidden_states[:num_scheduled_tokens]
2384
        pooling_metadata = self.input_batch.get_pooling_metadata()
2385
2386
2387
2388
        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]
2389

2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
        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()
2400

2401
        pooler_output: list[torch.Tensor | None] = []
2402
        for raw_output, seq_len, prompt_len in zip(
2403
2404
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2405
            output = raw_output if seq_len == prompt_len else None
2406
            pooler_output.append(output)
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416

        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,
        )

2417
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2418
2419
2420
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2421
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
2422
2423
2424
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2425
    def _preprocess(
2426
2427
        self,
        scheduler_output: "SchedulerOutput",
2428
        num_input_tokens: int,  # Padded
2429
        intermediate_tensors: IntermediateTensors | None = None,
2430
    ) -> tuple[
2431
2432
        torch.Tensor | None,
        torch.Tensor | None,
2433
        torch.Tensor,
2434
        IntermediateTensors | None,
2435
        dict[str, Any],
2436
        ECConnectorOutput | None,
2437
    ]:
2438
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2439
        is_first_rank = get_pp_group().is_first_rank
2440
        is_encoder_decoder = self.model_config.is_encoder_decoder
2441

2442
2443
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2444
2445
        ec_connector_output = None

2446
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
2447
            # Run the multimodal encoder if any.
2448
2449
2450
2451
2452
2453
            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)
2454

2455
2456
2457
            # 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.
2458
            inputs_embeds_scheduled = self.model.embed_input_ids(
2459
2460
2461
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2462
            )
2463

2464
            # TODO(woosuk): Avoid the copy. Optimize.
2465
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2466

2467
            input_ids = None
2468
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2469
2470
2471
2472
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2473
        elif self.enable_prompt_embeds and is_first_rank:
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
            # 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).
2486
2487
2488
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2489
                .squeeze(1)
2490
            )
2491
2492
2493
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2494
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2495
2496
2497
2498
2499
                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
2500
        else:
2501
2502
2503
2504
            # 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.
2505
            input_ids = self.input_ids.gpu[:num_input_tokens]
2506
            inputs_embeds = None
2507
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2508

2509
        if self.uses_mrope:
2510
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2511
2512
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
2513
        else:
2514
            positions = self.positions.gpu[:num_input_tokens]
2515

2516
        if is_first_rank:
2517
2518
            intermediate_tensors = None
        else:
2519
            assert intermediate_tensors is not None
2520
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2521
2522
                num_input_tokens, intermediate_tensors, True
            )
2523

2524
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
2525
2526
2527
2528
2529
2530
2531
            # Run the encoder, just like we do with other multimodal inputs.
            # For an encoder-decoder model, our processing here is a bit
            # simpler, because the outputs are just passed to the decoder.
            # We are not doing any prompt replacement. We also will only
            # ever have a single encoder input.
            encoder_outputs = self._execute_mm_encoder(scheduler_output)
            model_kwargs.update({"encoder_outputs": encoder_outputs})
2532

2533
2534
2535
2536
2537
2538
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2539
            ec_connector_output,
2540
        )
2541

2542
    def _sample(
2543
        self,
2544
2545
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2546
    ) -> SamplerOutput:
2547
        # Sample the next token and get logprobs if needed.
2548
        sampling_metadata = self.input_batch.sampling_metadata
2549
        if spec_decode_metadata is None:
2550
2551
2552
            # 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()
2553
            return self.sampler(
2554
2555
2556
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2557

2558
        sampler_output = self.rejection_sampler(
2559
2560
            spec_decode_metadata,
            None,  # draft_probs
2561
            logits,
2562
2563
            sampling_metadata,
        )
2564
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2565
2566
2567
        return sampler_output

    def _bookkeeping_sync(
2568
2569
2570
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2571
        logits: torch.Tensor | None,
2572
2573
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2574
        spec_decode_metadata: SpecDecodeMetadata | None,
2575
    ) -> tuple[
2576
        dict[str, int],
2577
        LogprobsLists | None,
2578
        list[list[int]],
2579
        dict[str, LogprobsTensors | None],
2580
2581
2582
        list[str],
        dict[str, int],
        list[int],
2583
    ]:
2584
2585
2586
2587
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2588
2589
2590
2591
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
2592
2593
2594
2595
        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)
2596

2597
2598
2599
        # 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()
2600
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2601
2602

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2603
        sampled_token_ids = sampler_output.sampled_token_ids
2604
        logprobs_tensors = sampler_output.logprobs_tensors
2605
        invalid_req_indices = []
2606
        cu_num_tokens: list[int] | None = None
2607
2608
2609
2610
2611
2612
        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)
2613
2614
2615
                # Mask out the sampled tokens that should not be sampled.
                for i in discard_sampled_tokens_req_indices:
                    valid_sampled_token_ids[int(i)].clear()
2616
2617
            else:
                # Includes spec decode tokens.
2618
                valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
2619
2620
                    sampled_token_ids,
                    self.input_batch.vocab_size,
2621
2622
                    discard_sampled_tokens_req_indices,
                    return_cu_num_tokens=logprobs_tensors is not None,
2623
                )
2624
        else:
2625
            valid_sampled_token_ids = []
2626
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2627
2628
2629
2630
2631
            invalid_req_indices_set = set(invalid_req_indices)

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
2632
2633
2634
2635
            # With spec decoding, this is done in propose_draft_token_ids().
            if self.input_batch.prev_sampled_token_ids is None:
                assert sampled_token_ids.shape[-1] == 1
                self.input_batch.prev_sampled_token_ids = sampled_token_ids
2636
2637
2638
2639
2640
            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
            }
2641

2642
2643
2644
2645
2646
        # 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.
2647
        req_ids = self.input_batch.req_ids
2648
2649
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2650
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2651
2652
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2653

2654
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
2655

2656
            if not sampled_ids:
2657
2658
2659
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2660
            end_idx = start_idx + num_sampled_ids
2661
2662
2663
2664
            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}"
2665
            )
2666

2667
2668
            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
2669
2670
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2671

2672
            req_id = req_ids[req_idx]
2673
2674
2675
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2676
        logprobs_lists = (
2677
            logprobs_tensors.tolists(cu_num_tokens)
2678
            if not self.use_async_scheduling and logprobs_tensors is not None
2679
2680
2681
2682
2683
2684
2685
2686
2687
            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,
        )

2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
        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,
        )

2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
    @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()

2713
2714
    def _model_forward(
        self,
2715
2716
2717
2718
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2719
2720
2721
2722
2723
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2724
        Motivation: We can inspect only this method versus
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
        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,
        )

2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
    def _determine_batch_execution_and_padding(
        self,
        num_tokens: int,
        num_reqs: int,
        num_scheduled_tokens_np: np.ndarray,
        max_num_scheduled_tokens: int,
        use_cascade_attn: bool,
        allow_microbatching: bool = True,
        force_eager: bool = False,
        # For cudagraph capture TODO(lucas): Refactor how we capture cudagraphs (will
        # be improved in model runner v2)
        force_uniform_decode: bool | None = None,
        force_has_lora: bool | None = None,
    ) -> tuple[
2759
2760
2761
2762
2763
        CUDAGraphMode,
        BatchDescriptor,
        UBatchSlices | None,
        torch.Tensor | None,
        CUDAGraphStat | None,
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
    ]:
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
        uniform_decode = (
            (
                (max_num_scheduled_tokens == self.uniform_decode_query_len)
                and (num_tokens_padded == max_num_scheduled_tokens * num_reqs)
            )
            if force_uniform_decode is None
            else force_uniform_decode
        )

        has_lora = (
            len(self.input_batch.lora_id_to_lora_request) > 0
            if force_has_lora is None
            else force_has_lora
        )

        dispatch_cudagraph = (
            lambda num_tokens: self.cudagraph_dispatcher.dispatch(
                num_tokens=num_tokens,
                has_lora=has_lora,
                use_cascade_attn=use_cascade_attn,
                uniform_decode=uniform_decode,
            )
            if not force_eager
            else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
        )

        cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
        num_tokens_padded = batch_descriptor.num_tokens

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
        ubatch_slices, num_tokens_across_dp = None, None
        if self.vllm_config.parallel_config.data_parallel_size > 1:
            # Disable DP padding when running eager to avoid excessive padding when
            # running prefills. This lets us set cudagraph_mode="NONE" 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
            )

            ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
                num_tokens_unpadded=num_tokens_padded,
                parallel_config=self.parallel_config,
                allow_microbatching=allow_microbatching,
                allow_dp_padding=allow_dp_padding,
                num_tokens_padded=num_tokens_padded,
                uniform_decode=uniform_decode,
                num_scheduled_tokens_per_request=num_scheduled_tokens_np,
            )

            # Extract DP padding if there is any
            if num_tokens_across_dp is not None:
                dp_rank = self.parallel_config.data_parallel_rank
                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())

                # Re-dispatch with DP padding
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
                # Assert to make sure the agreed upon token count is correct otherwise
                # num_tokens_across_dp will no-longer be valid
                assert batch_descriptor.num_tokens == num_tokens_padded

2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
        cudagraph_stats = None
        if self.vllm_config.observability_config.cudagraph_metrics:
            cudagraph_stats = CUDAGraphStat(
                num_unpadded_tokens=num_tokens,
                num_padded_tokens=batch_descriptor.num_tokens,
                num_paddings=batch_descriptor.num_tokens - num_tokens,
                runtime_mode=str(cudagraph_mode),
            )

        return (
            cudagraph_mode,
            batch_descriptor,
            ubatch_slices,
            num_tokens_across_dp,
            cudagraph_stats,
        )
2844

2845
2846
2847
2848
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2849
        intermediate_tensors: IntermediateTensors | None = None,
2850
2851
2852
2853
2854
2855
    ) -> 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."
            )
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870

        # self._draft_token_ids is None when `input_fits_in_drafter=False`
        # and there is no draft tokens scheduled. so it need to update the
        # spec_decoding info in scheduler_output with async_scheduling.
        # use deepcopy to avoid the modification has influence on the
        # scheduler_output in engine core process.
        # TODO(Ronald1995): deepcopy is expensive when there is a large
        # number of requests, optimize it later.
        if (
            self.use_async_scheduling
            and self.num_spec_tokens
            and self._draft_token_ids is None
        ):
            scheduler_output = deepcopy(scheduler_output)

2871
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2872
        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
2873
2874
2875
2876
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

2877
2878
2879
2880
2881
2882
2883
2884
                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)

2885
                if not num_scheduled_tokens:
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
                    if (
                        self.parallel_config.distributed_executor_backend
                        == "external_launcher"
                        and self.parallel_config.data_parallel_size > 1
                    ):
                        # this is a corner case when both external launcher
                        # and DP are enabled, num_scheduled_tokens could be
                        # 0, and has_unfinished_requests in the outer loop
                        # returns True. before returning early here we call
                        # dummy run to ensure coordinate_batch_across_dp
                        # is called into to avoid out of sync issues.
2897
                        self._dummy_run(1)
2898
2899
2900
2901
                    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(
2902
2903
                        scheduler_output, self.vllm_config
                    )
2904
                if self.cache_config.kv_sharing_fast_prefill:
2905
                    assert not self.num_prompt_logprobs, (
2906
2907
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2908
2909
                        "it when the requests need prompt logprobs"
                    )
2910

2911
2912
2913
2914
2915
                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())
2916
                num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
2917

2918
2919
2920
                (
                    logits_indices,
                    spec_decode_metadata,
2921
                ) = self._prepare_inputs(
2922
2923
                    scheduler_output,
                    num_scheduled_tokens_np,
2924
2925
2926
2927
                )

                cascade_attn_prefix_lens = None
                # Disable cascade attention when using microbatching (DBO)
2928
                if self.cascade_attn_enabled and not self.parallel_config.enable_dbo:
2929
2930
2931
                    # Pre-compute cascade attention prefix lengths
                    cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
                        num_scheduled_tokens_np,
2932
                        self.input_batch.num_computed_tokens_cpu[:num_reqs],
2933
2934
2935
                        scheduler_output.num_common_prefix_blocks,
                    )

2936
2937
2938
2939
2940
                (
                    cudagraph_mode,
                    batch_desc,
                    ubatch_slices,
                    num_tokens_across_dp,
2941
                    cudagraph_stats,
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
                ) = self._determine_batch_execution_and_padding(
                    num_tokens=num_tokens_unpadded,
                    num_reqs=num_reqs,
                    num_scheduled_tokens_np=num_scheduled_tokens_np,
                    max_num_scheduled_tokens=max_num_scheduled_tokens,
                    use_cascade_attn=cascade_attn_prefix_lens is not None,
                )

                logger.debug(
                    "Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
                    "ubatch_slices: %s, num_tokens_across_dp: %s",
                    cudagraph_mode,
                    batch_desc,
                    ubatch_slices,
                    num_tokens_across_dp,
                )

                num_tokens_padded = batch_desc.num_tokens
                num_reqs_padded = (
                    batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
                )
2963
2964

                use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2965
2966
2967
                pad_attn = cudagraph_mode == CUDAGraphMode.FULL

                (attn_metadata, spec_decode_common_attn_metadata) = (
2968
                    self._build_attention_metadata(
2969
2970
                        num_tokens=num_tokens_unpadded,
                        num_tokens_padded=num_tokens_padded if pad_attn else None,
2971
                        num_reqs=num_reqs,
2972
2973
                        num_reqs_padded=num_reqs_padded if pad_attn else None,
                        max_query_len=max_num_scheduled_tokens,
2974
2975
2976
                        ubatch_slices=ubatch_slices,
                        logits_indices=logits_indices,
                        use_spec_decode=use_spec_decode,
2977
                        num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
2978
2979
2980
                        cascade_attn_prefix_lens=cascade_attn_prefix_lens,
                    )
                )
2981

2982
2983
2984
2985
2986
2987
2988
2989
2990
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
2991
            )
2992

2993
        # Set cudagraph mode to none if calc_kv_scales is true.
2994
2995
2996
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
2997
            cudagraph_mode = CUDAGraphMode.NONE
2998
2999
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
3000

3001
3002
        # Run the model.
        # Use persistent buffers for CUDA graphs.
3003
3004
        with (
            set_forward_context(
3005
3006
                attn_metadata,
                self.vllm_config,
3007
                num_tokens=num_tokens_padded,
3008
                num_tokens_across_dp=num_tokens_across_dp,
3009
3010
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
3011
                ubatch_slices=ubatch_slices,
3012
            ),
3013
            record_function_or_nullcontext("gpu_model_runner: forward"),
3014
3015
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
3016
            model_output = self._model_forward(
3017
3018
3019
3020
3021
3022
3023
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3024
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3025
            if self.use_aux_hidden_state_outputs:
3026
                # True when EAGLE 3 is used.
3027
3028
                hidden_states, aux_hidden_states = model_output
            else:
3029
                # Common case.
3030
3031
3032
                hidden_states = model_output
                aux_hidden_states = None

3033
3034
3035
3036
3037
            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)
3038
                    hidden_states.kv_connector_output = kv_connector_output
3039
                    self.kv_connector_output = kv_connector_output
3040
                    return hidden_states
3041

3042
                if self.is_pooling_model:
3043
                    # Return the pooling output.
3044
3045
3046
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
3047
3048
                    output.kv_connector_output = kv_connector_output
                    return output
3049
3050

                sample_hidden_states = hidden_states[logits_indices]
3051
                logits = self.model.compute_logits(sample_hidden_states)
3052
3053
3054
3055
            else:
                # Rare case.
                assert not self.is_pooling_model

3056
                sample_hidden_states = hidden_states[logits_indices]
3057
                if not get_pp_group().is_last_rank:
3058
                    all_gather_tensors = {
3059
                        "residual": not is_residual_scattered_for_sp(
3060
                            self.vllm_config, num_tokens_padded
3061
                        )
3062
                    }
3063
                    get_pp_group().send_tensor_dict(
3064
3065
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3066
3067
                        all_gather_tensors=all_gather_tensors,
                    )
3068
3069
                    logits = None
                else:
3070
                    logits = self.model.compute_logits(sample_hidden_states)
3071

3072
                model_output_broadcast_data: dict[str, Any] = {}
3073
3074
3075
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3076
                broadcasted = get_pp_group().broadcast_tensor_dict(
3077
3078
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3079
3080
                assert broadcasted is not None
                logits = broadcasted["logits"]
3081

3082
3083
3084
3085
3086
3087
3088
3089
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3090
            ec_connector_output,
3091
            cudagraph_stats,
3092
        )
3093
        self.kv_connector_output = kv_connector_output
3094
3095
3096
3097
3098
3099
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
3100
3101
3102
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

3103
3104
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
3105
            if not kv_connector_output:
3106
                return None  # type: ignore[return-value]
3107
3108
3109
3110
3111
3112
3113
3114
3115

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

            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
            output.kv_connector_output = kv_connector_output
            return output
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3126
            ec_connector_output,
3127
            cudagraph_stats,
3128
3129
3130
3131
3132
3133
3134
3135
3136
        ) = 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
            )
3137

3138
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3139
3140
            sampler_output = self._sample(logits, spec_decode_metadata)

3141
3142
        self.input_batch.prev_sampled_token_ids = None

3143
        def propose_draft_token_ids(sampled_token_ids):
3144
            assert spec_decode_common_attn_metadata is not None
3145
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
                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,
                )

3157
        spec_config = self.speculative_config
3158
        use_padded_batch_for_eagle = (
3159
3160
3161
            spec_config is not None
            and spec_config.use_eagle()
            and not spec_config.disable_padded_drafter_batch
3162
        )
3163
3164
3165
        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
3166
        if (
3167
3168
3169
            spec_config is not None
            and spec_config.draft_model_config is not None
            and spec_config.draft_model_config.max_model_len is not None
3170
        ):
3171
            effective_drafter_max_model_len = (
3172
                spec_config.draft_model_config.max_model_len
3173
            )
3174
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
3175
            spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
3176
3177
            <= effective_drafter_max_model_len
        )
3178
        if use_padded_batch_for_eagle:
3179
3180
            assert self.speculative_config is not None
            assert isinstance(self.drafter, EagleProposer)
3181
3182
3183
3184
3185
3186
            sampled_token_ids = sampler_output.sampled_token_ids
            if input_fits_in_drafter:
                # EAGLE speculative decoding can use the GPU sampled tokens
                # as inputs, and does not need to wait for bookkeeping to finish.
                propose_draft_token_ids(sampled_token_ids)
            elif self.valid_sampled_token_count_event is not None:
3187
                assert spec_decode_common_attn_metadata is not None
3188
3189
3190
3191
3192
3193
                next_token_ids, valid_sampled_tokens_count = (
                    self.drafter.prepare_next_token_ids_padded(
                        spec_decode_common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
3194
                        self.discard_request_mask.gpu,
3195
3196
3197
3198
3199
                    )
                )
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
3200

3201
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
3202
3203
3204
3205
3206
3207
3208
3209
            (
                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,
3210
3211
3212
3213
3214
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
3215
                scheduler_output.total_num_scheduled_tokens,
3216
                spec_decode_metadata,
3217
            )
3218

3219
3220
3221
3222
3223
        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
3224
3225
3226
            # 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)
3227

3228
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
3229
            self.eplb_step()
3230
3231
3232
3233
3234
3235
3236
3237
3238
        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,
3239
3240
3241
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
3242
                num_nans_in_logits=num_nans_in_logits,
3243
                cudagraph_stats=cudagraph_stats,
3244
            )
3245

3246
3247
        if not self.use_async_scheduling:
            return output
3248
3249
3250
3251
3252
3253
3254
3255
3256
        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,
3257
                vocab_size=self.input_batch.vocab_size,
3258
3259
3260
3261
3262
            )
        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
3263
            # any requests with sampling params that require output ids.
3264
3265
3266
3267
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
3268
3269
3270

        return async_output

3271
    def take_draft_token_ids(self) -> DraftTokenIds | None:
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
        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)

3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
    def _copy_valid_sampled_token_count(
        self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
    ) -> None:
        if self.valid_sampled_token_count_event is None:
            return

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

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

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

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

3313
3314
3315
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
3316
        sampled_token_ids: torch.Tensor | list[list[int]],
3317
3318
3319
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
3320
3321
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3322
        common_attn_metadata: CommonAttentionMetadata,
3323
    ) -> list[list[int]] | torch.Tensor:
3324
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3325
3326
3327
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
3328
            assert isinstance(sampled_token_ids, list)
3329
            assert isinstance(self.drafter, NgramProposer)
3330
            draft_token_ids = self.drafter.propose(
3331
3332
                sampled_token_ids,
                self.input_batch.req_ids,
3333
3334
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
3335
3336
                self.input_batch.spec_decode_unsupported_reqs,
            )
3337
        elif spec_config.method == "suffix":
3338
3339
3340
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
3341
        elif spec_config.method == "medusa":
3342
            assert isinstance(sampled_token_ids, list)
3343
            assert isinstance(self.drafter, MedusaProposer)
3344

3345
3346
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
3347
3348
3349
3350
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
3351
3352
3353
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
3354
                for num_draft, tokens in zip(
3355
3356
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
3357
                    indices.append(offset + len(tokens) - 1)
3358
                    offset += num_draft + 1
3359
                indices = torch.tensor(indices, device=self.device)
3360
3361
                hidden_states = sample_hidden_states[indices]

3362
            draft_token_ids = self.drafter.propose(
3363
3364
3365
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
3366
        elif spec_config.use_eagle():
3367
            assert isinstance(self.drafter, EagleProposer)
3368

3369
            if spec_config.disable_padded_drafter_batch:
3370
3371
3372
                # 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.
3373
3374
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
3375
                    "padded-batch is disabled."
3376
                )
3377
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
3378
3379
3380
3381
3382
                    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"
3390
                    "padded-batch is enabled."
3391
3392
                )
                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,
3398
                        self.discard_request_mask.gpu,
3399
                    )
3400
                )
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                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
3404

3405
            if spec_decode_metadata is None:
3406
                token_indices_to_sample = None
3407
                # input_ids can be None for multimodal models.
3408
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
3409
                target_positions = self._get_positions(num_scheduled_tokens)
3410
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3411
                    assert aux_hidden_states is not None
3412
                    target_hidden_states = torch.cat(
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                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
3415
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
3417
            else:
3418
                if spec_config.disable_padded_drafter_batch:
3419
                    token_indices_to_sample = None
3420
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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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                    target_token_ids = self.input_ids.gpu[token_indices]
                    target_positions = self._get_positions(token_indices)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[token_indices] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[token_indices]
3434
                else:
3435
                    common_attn_metadata, 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,
                        )
                    )
3442
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                    total_num_tokens = common_attn_metadata.num_actual_tokens
                    # When padding the batch, token_indices is just a range
                    target_token_ids = self.input_ids.gpu[:total_num_tokens]
                    target_positions = self._get_positions(total_num_tokens)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[:total_num_tokens] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[:total_num_tokens]
3453

3454
            if self.supports_mm_inputs:
3455
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
3461

3462
            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,
3467
                last_token_indices=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
3469
                common_attn_metadata=common_attn_metadata,
3470
                mm_embed_inputs=mm_embed_inputs,
3471
            )
3472

3473
        return draft_token_ids
3474

3475
3476
3477
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
3478
3479
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
3480
                f"Allowed configs: {allowed_config_names}"
3481
            )
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

3486
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3489
3490
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
3491
3492
3493
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3495
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
3496
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3500
        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)
        )
3501

3502
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3504
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
3505
        with DeviceMemoryProfiler() as m:
3506
            time_before_load = time.perf_counter()
3507
            model_loader = get_model_loader(self.load_config)
3508
            self.model = model_loader.load_model(
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3510
                vllm_config=self.vllm_config, model_config=self.model_config
            )
3511
            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3515
            if hasattr(self, "drafter"):
3516
                logger.info_once("Loading drafter model...")
3517
                self.drafter.load_model(self.model)
3518
3519
3520
3521
3522
                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
3523
3524
3525
                    spec_config = self.vllm_config.speculative_config
                    assert spec_config is not None
                    assert spec_config.draft_model_config is not None
3526
3527
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
3528
                        spec_config.draft_model_config.model,
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3530
3531
3532
3533
3534
3535
3536
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3542
3543
3544
                    )

                    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,
3545
                        spec_config.draft_model_config,
3546
3547
3548
3549
3550
3551
                        global_expert_load,
                        old_global_expert_indices,
                        rank_mapping,
                    )
                    eplb_models += 1

3552
            if self.use_aux_hidden_state_outputs:
3553
                if not supports_eagle3(self.get_model()):
3554
3555
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
3556
3557
                        "aux_hidden_state_outputs was requested"
                    )
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570

                # 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)
3571
            time_after_load = time.perf_counter()
3572
        self.model_memory_usage = m.consumed_memory
3573
        logger.info_once(
3574
            "Model loading took %.4f GiB memory and %.6f seconds",
3575
3576
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3577
            scope="local",
3578
        )
3579
        prepare_communication_buffer_for_model(self.model)
3580
3581
3582
3583
        if (drafter := getattr(self, "drafter", None)) and (
            drafter_model := getattr(drafter, "model", None)
        ):
            prepare_communication_buffer_for_model(drafter_model)
3584
        mm_config = self.model_config.multimodal_config
3585
        self.is_multimodal_pruning_enabled = (
3586
            supports_multimodal_pruning(self.get_model())
3587
3588
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
3589
        )
3590

3591
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
            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(
3603
                self.model,
3604
                self.model_config,
3605
3606
3607
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3608
            )
3609
3610
            if self.eplb_state.is_async:
                self.eplb_state.start_async_loop(rank_mapping=rank_mapping)
3611

3612
        if (
3613
3614
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3615
            and supports_dynamo()
3616
        ):
3617
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3618
            compilation_counter.stock_torch_compile_count += 1
3619
            self.model.compile(fullgraph=True, backend=backend)
3620
            return
3621
        # for other compilation modes, cudagraph behavior is controlled by
3622
3623
3624
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3625
3626
3627
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
        if cudagraph_mode.has_full_cudagraphs() and not self.parallel_config.enable_dbo:
3628
3629
3630
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3631
        elif self.parallel_config.enable_dbo:
3632
            if cudagraph_mode.has_full_cudagraphs():
3633
3634
3635
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3636
            else:
3637
3638
3639
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3640

3641
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
        """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

3665
    def reload_weights(self) -> None:
3666
        assert getattr(self, "model", None) is not None, (
3667
            "Cannot reload weights before model is loaded."
3668
        )
3669
3670
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3671
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3672

3673
3674
3675
3676
3677
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3678
            self.get_model(),
3679
            tensorizer_config=tensorizer_config,
3680
            model_config=self.model_config,
3681
3682
        )

3683
3684
3685
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3686
        num_scheduled_tokens: dict[str, int],
3687
    ) -> dict[str, LogprobsTensors | None]:
3688
        num_prompt_logprobs_dict = self.num_prompt_logprobs
3689
3690
3691
        if not num_prompt_logprobs_dict:
            return {}

3692
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3693
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3694
3695
3696
3697
3698

        # 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():
3699
3700
3701
3702
            num_tokens = num_scheduled_tokens.get(req_id)
            if num_tokens is None:
                # This can happen if the request was preempted in prefill stage.
                continue
3703
3704
3705

            # Get metadata for this request.
            request = self.requests[req_id]
3706
3707
3708
3709
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3710
3711
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3712
3713
                self.device, non_blocking=True
            )
3714

3715
3716
3717
3718
3719
3720
            # 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(
3721
3722
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3723
3724
                in_progress_dict[req_id] = logprobs_tensors

3725
            # Determine number of logits to retrieve.
3726
3727
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3728
            num_remaining_tokens = num_prompt_tokens - start_tok
3729
            if num_tokens <= num_remaining_tokens:
3730
                # This is a chunk, more tokens remain.
3731
3732
3733
                # 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.
3734
3735
3736
3737
3738
                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)
3739
3740
3741
3742
3743
3744
3745
                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
3746
3747
3748
3749
3750

            # 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]
3751
            offset = self.query_start_loc.np[req_idx].item()
3752
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3753
            logits = self.model.compute_logits(prompt_hidden_states)
3754
3755
3756
3757

            # 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.
3758
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3759
3760

            # Compute prompt logprobs.
3761
3762
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3763
3764
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3765
3766

            # Transfer GPU->CPU async.
3767
3768
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3769
3770
3771
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3772
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3773
3774
                ranks, non_blocking=True
            )
3775
3776
3777
3778
3779

        # 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]
3780
            del in_progress_dict[req_id]
3781
3782

        # Must synchronize the non-blocking GPU->CPU transfers.
3783
        if prompt_logprobs_dict:
3784
            self._sync_device()
3785
3786
3787

        return prompt_logprobs_dict

3788
3789
    def _get_nans_in_logits(
        self,
3790
        logits: torch.Tensor | None,
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
    ) -> 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])
3802
3803
3804
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3805
3806
3807
3808
            return num_nans_in_logits
        except IndexError:
            return {}

3809
3810
3811
3812
3813
3814
    @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
3815
         - during DP rank dummy run
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
        """
        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(
3827
                    self.input_ids.gpu,
3828
3829
                    low=0,
                    high=self.model_config.get_vocab_size(),
3830
3831
                    dtype=input_ids.dtype,
                )
3832

3833
            logger.debug_once("Randomizing dummy data for DP Rank")
3834
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3835
3836
3837
            yield
            input_ids.fill_(0)

3838
3839
3840
3841
3842
3843
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3844
3845
        assert self.mm_budget is not None

3846
3847
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3848
            seq_len=self.max_model_len,
3849
            mm_counts={modality: 1},
3850
            cache=self.mm_budget.cache,
3851
3852
3853
3854
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3855
3856
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3857

3858
        model = cast(SupportsMultiModal, self.model)
3859
3860
3861
3862
3863
3864
3865
        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,
3866
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3867
3868
            )
        )
3869

3870
3871
3872
3873
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3874
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3875
3876
        force_attention: bool = False,
        uniform_decode: bool = False,
3877
        allow_microbatching: bool = True,
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        skip_eplb: bool = False,
        is_profile: bool = False,
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        create_mixed_batch: bool = False,
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        remove_lora: bool = True,
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        activate_lora: bool = False,
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        is_graph_capturing: bool = False,
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    ) -> tuple[torch.Tensor, torch.Tensor]:
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        """
        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.
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                - if not set will determine the cudagraph mode based on using
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                    the self.cudagraph_dispatcher.
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                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
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            force_attention: If True, always create attention metadata. Used to
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                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.
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            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
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            remove_lora: If False, dummy LoRAs are not destroyed after the run
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            activate_lora: If False, dummy_run is performed without LoRAs.
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        """
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        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
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        # If cudagraph_mode.decode_mode() == FULL and
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        # cudagraph_mode.separate_routine(). This means that we are using
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        # 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.
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        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
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        # 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
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        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
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            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
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            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
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            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
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            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
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            assert not create_mixed_batch
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            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
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            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
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                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
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        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

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        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
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        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
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        num_tokens_unpadded = int(num_scheduled_tokens.sum())

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        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3963

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        _cudagraph_mode, batch_desc, ubatch_slices, num_tokens_across_dp, _ = (
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            self._determine_batch_execution_and_padding(
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs,
                num_scheduled_tokens_np=num_scheduled_tokens,
                max_num_scheduled_tokens=max_query_len,
                use_cascade_attn=False,
                allow_microbatching=allow_microbatching,
                force_eager=is_profile
                or (cudagraph_runtime_mode == CUDAGraphMode.NONE),
                # `force_uniform_decode` is used for cudagraph capture; because for
                # capturing mixed prefill-decode batches, we sometimes use
                # num_tokens == num_reqs which looks like a uniform decode batch to the
                # dispatcher; but we actually want to capture a piecewise cudagraph
                force_uniform_decode=uniform_decode,
                # `force_has_lora` is used for cudagraph capture; because LoRA is
                # activated later in the context manager, but we need to know the
                # LoRA state when determining the batch descriptor for capture
                force_has_lora=activate_lora,
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            )
        )
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        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
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        else:
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            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )

        num_tokens_padded = batch_desc.num_tokens
        num_reqs_padded = (
            batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
        )
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        attn_metadata: PerLayerAttnMetadata | None = None
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        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
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        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
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            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:
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                seq_lens = max_query_len  # type: ignore[assignment]
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            self.seq_lens.np[:num_reqs] = seq_lens
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            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
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            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
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            self.query_start_loc.copy_to_gpu()

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            attn_metadata, _ = self._build_attention_metadata(
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                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
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                ubatch_slices=ubatch_slices,
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                for_cudagraph_capture=is_graph_capturing,
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            )
4026

4027
        with self.maybe_dummy_run_with_lora(
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            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
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        ):
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            # Make sure padding doesn't exceed max_num_tokens
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            assert num_tokens_padded <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_padded)
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            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
4038
                input_ids = None
4039
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
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                model_kwargs = {
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                    **model_kwargs,
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                    **self._dummy_mm_kwargs(num_reqs),
                }
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            elif self.enable_prompt_embeds:
                input_ids = None
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                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
                model_kwargs = self._init_model_kwargs(num_tokens_padded)
4048
            else:
4049
                input_ids = self.input_ids.gpu[:num_tokens_padded]
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                inputs_embeds = None
4051

4052
            if self.uses_mrope:
4053
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
4054
            elif self.uses_xdrope_dim > 0:
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                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
4056
            else:
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                positions = self.positions.gpu[:num_tokens_padded]
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            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,
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                            device=self.device,
                        )
                    )
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                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
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                    num_tokens_padded, None, False
4073
                )
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4075
            if ubatch_slices is not None:
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                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
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                num_tokens_padded = ubatch_slices[0].num_tokens
4080
                if num_tokens_across_dp is not None:
4081
                    num_tokens_across_dp[:] = num_tokens_padded
4082

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            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
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                    attn_metadata,
                    self.vllm_config,
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                    num_tokens=num_tokens_padded,
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                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
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                    batch_descriptor=batch_desc,
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                    ubatch_slices=ubatch_slices,
                ),
            ):
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                outputs = self.model(
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                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
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                    **model_kwargs,
4101
                )
4102

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4106
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
4107

4108
            if self.speculative_config and self.speculative_config.use_eagle():
4109
                assert isinstance(self.drafter, EagleProposer)
4110
                use_cudagraphs = (
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4111
                    cudagraph_runtime_mode.has_mode(CUDAGraphMode.PIECEWISE)
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                    and not self.speculative_config.enforce_eager
                )
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                # 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,
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4125
                    is_graph_capturing=is_graph_capturing,
4126
                )
4127

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        # 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)

4138
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
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4142
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
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4148

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
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4152
        # 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)
4153

4154
        logits = self.model.compute_logits(hidden_states)
4155
4156
        num_reqs = logits.size(0)

4157
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
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4172

        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)],
4173
            spec_token_ids=[[] for _ in range(num_reqs)],
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4175
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
4176
            logitsprocs=LogitsProcessors(),
4177
        )
4178
        try:
4179
4180
4181
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
4182
        except RuntimeError as e:
4183
            if "out of memory" in str(e):
4184
4185
4186
4187
                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 "
4188
4189
                    "initializing the engine."
                ) from e
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4191
            else:
                raise e
4192
        if self.speculative_config:
4193
4194
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
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                draft_token_ids, self.device
            )
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4200
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4202

            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
4203
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            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
4208
            )
4209
4210
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            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
4212
                logits,
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4214
                dummy_metadata,
            )
4215
        return sampler_output
4216

4217
    def _dummy_pooler_run_task(
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4219
        self,
        hidden_states: torch.Tensor,
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4221
        task: PoolingTask,
    ) -> PoolerOutput:
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4224
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4231
4232
        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

4233
        dummy_prompt_lens = torch.tensor(
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4235
            num_scheduled_tokens_list,
            device="cpu",
4236
        )
4237
4238
4239
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
4240

4241
        model = cast(VllmModelForPooling, self.get_model())
4242
        dummy_pooling_params = PoolingParams(task=task)
4243
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
4244
        to_update = model.pooler.get_pooling_updates(task)
4245
4246
        to_update.apply(dummy_pooling_params)

4247
        dummy_metadata = PoolingMetadata(
4248
4249
4250
4251
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
4252

4253
4254
4255
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
4256

4257
        try:
4258
4259
4260
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
4261
        except RuntimeError as e:
4262
            if "out of memory" in str(e):
4263
                raise RuntimeError(
4264
4265
4266
                    "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 "
4267
4268
                    "initializing the engine."
                ) from e
4269
4270
            else:
                raise e
4271
4272
4273
4274
4275
4276
4277

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
4278
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4280
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
4281
            if self.scheduler_config.enable_chunked_prefill:
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                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks with chunked prefill enabled. "
                    "Please add --no-enable-chunked-prefill to your "
                    "config or CLI args. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )
            else:
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )

4298
        output_size = dict[PoolingTask, float]()
4299
        for task in supported_pooling_tasks:
4300
4301
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
4302
            output_size[task] = sum(o.nbytes for o in output)
4303
4304
4305
4306
            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)
4307

4308
    def profile_run(self) -> None:
4309
        # Profile with multimodal encoder & encoder cache.
4310
        if self.supports_mm_inputs:
4311
4312
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
4313
                logger.info(
4314
                    "Skipping memory profiling for multimodal encoder and "
4315
4316
                    "encoder cache."
                )
4317
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4319
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4321
4322
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4324
            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.
4325
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
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                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
4329
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4333
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4335
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4337

                    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,
                    )
4338

4339
4340
4341
4342
4343
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
4344

4345
                    # Run multimodal encoder.
4346
                    dummy_encoder_outputs = self.model.embed_multimodal(
4347
4348
                        **batched_dummy_mm_inputs
                    )
4349

4350
4351
4352
4353
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
4354

4355
4356
4357
                    # 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
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                    # (max_tokens_for_modality, hidden_size) and scatter
                    # encoder output into it.
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                    encoder_output_shape = dummy_encoder_outputs[0].shape
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                    max_mm_tokens_per_item = mm_budget.max_tokens_by_modality[
                        dummy_modality
                    ]
                    if encoder_output_shape[0] < max_mm_tokens_per_item:
                        encoder_hidden_size = encoder_output_shape[-1]
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                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
4369
                                (max_mm_tokens_per_item, encoder_hidden_size)
4370
                            )
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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.
4378
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
4379

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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
        )
4384
        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)
4389
        else:
4390
            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()
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        gc.collect()
4395

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    def capture_model(self) -> int:
4397
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
4398
            logger.warning(
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                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
4402
            return 0
4403

4404
4405
        compilation_counter.num_gpu_runner_capture_triggers += 1

4406
4407
        start_time = time.perf_counter()

4408
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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()
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                    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):
4429
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
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            cudagraph_mode = self.compilation_config.cudagraph_mode
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            assert cudagraph_mode is not None
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            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

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            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
4443
                # 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,
                )
4452

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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
4465
                    if max_num_tokens >= x >= self.uniform_decode_query_len
4466
                ]
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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.
4490
        logger.info_once(
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            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4494
            scope="local",
4495
        )
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        return cuda_graph_size
4497

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    def _capture_cudagraphs(
        self,
4500
        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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4512
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4514
4515

        # 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,
                ),
            )
4519

4520
        # We skip EPLB here since we don't want to record dummy metrics
4521
        for num_tokens, activate_lora in compilation_cases:
4522
            # 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,
                )
4535
            )
4536

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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,
4552
                    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,
4561
                activate_lora=activate_lora,
Rémi Delacourt's avatar
Rémi Delacourt committed
4562
                is_graph_capturing=True,
4563
            )
4564
        self.maybe_remove_all_loras(self.lora_config)
4565

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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"
4571

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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,
4578
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4579
            layer_type = cast(type[Any], AttentionLayerBase)
4580
            layers = get_layers_from_vllm_config(
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                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
4582
            )
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
4585
            # 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.
4590
            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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4593
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4595

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
4596
                        attn_backend,  # type: ignore[arg-type]
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4598
                    )

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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):
4602
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4603
                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4607
                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()),
            )
4612
4613

        def create_attn_groups(
4614
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4615
            kv_cache_group_id: int,
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4617
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4618
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4619
                attn_group = AttentionGroup(
4620
                    attn_backend,
4621
                    layer_names,
4622
                    kv_cache_spec,
4623
                    kv_cache_group_id,
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4625
                )

4626
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4628
                attn_groups.append(attn_group)
            return attn_groups

4629
        attention_backend_maps = []
4630
        attention_backend_list = []
4631
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4632
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4633
            attention_backend_maps.append(attn_backends[0])
4634
            attention_backend_list.append(attn_backends[1])
4635
4636

        # Resolve cudagraph_mode before actually initialize metadata_builders
4637
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        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4640

4641
4642
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4643

4644
4645
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4659
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4661
    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
4662
        # Calculate reorder batch threshold (if needed)
4663
4664
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
4665
4666
        self.calculate_reorder_batch_threshold()

4667
    def _check_and_update_cudagraph_mode(
4668
4669
4670
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4671
    ) -> None:
4672
        """
4673
        Resolve the cudagraph_mode when there are multiple attention
4674
        groups with potential conflicting CUDA graph support.
4675
4676
4677
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4678
        min_cg_support = AttentionCGSupport.ALWAYS
4679
        min_cg_backend_name = None
4680

4681
4682
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4684
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4687
4688
4689
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4692
        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__
4693
4694
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
4695
        assert cudagraph_mode is not None
4696
        # check cudagraph for mixed batch is supported
4697
4698
4699
4700
4701
4702
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4703
                f"with {min_cg_backend_name} backend (support: "
4704
4705
                f"{min_cg_support})"
            )
4706
4707
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4708
4709
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4710
                    "make sure compilation mode is VLLM_COMPILE"
4711
                )
4712
4713
4714
4715
4716
                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"
4717
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4718
                    CUDAGraphMode.FULL_AND_PIECEWISE
4719
                )
4720
4721
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4722
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4723
                    CUDAGraphMode.FULL_DECODE_ONLY
4724
                )
4725
4726
            logger.warning(msg)

4727
        # check that if we are doing decode full-cudagraphs it is supported
4728
4729
4730
4731
4732
4733
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4734
                f"with {min_cg_backend_name} backend (support: "
4735
4736
                f"{min_cg_support})"
            )
4737
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4738
4739
4740
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4742
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4743
                    "attention is compiled piecewise"
4744
4745
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4746
                    CUDAGraphMode.PIECEWISE
4747
                )
4748
            else:
4749
4750
                msg += (
                    "; setting cudagraph_mode=NONE because "
4751
                    "attention is not compiled piecewise"
4752
4753
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4754
                    CUDAGraphMode.NONE
4755
                )
4756
4757
            logger.warning(msg)

4758
4759
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4760
4761
4762
4763
4764
4765
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4767
        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 "
4768
                f"{min_cg_backend_name} (support: {min_cg_support})"
4769
            )
4770
4771
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4772
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4773
                    CUDAGraphMode.PIECEWISE
4774
                )
4775
4776
            else:
                msg += "; setting cudagraph_mode=NONE"
4777
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4778
                    CUDAGraphMode.NONE
4779
                )
4780
4781
4782
4783
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4784
4785
4786
4787
4788
4789
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4790
                f"supported with {min_cg_backend_name} backend ("
4791
4792
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4793
                "and make sure compilation mode is VLLM_COMPILE"
4794
            )
4795

4796
4797
4798
4799
4800
4801
4802
4803
4804
4805
4806
4807
4808
4809
        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
        # we need to adjust the cudagraph sizes to be a multiple of the uniform
        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
        # Will be removed in the near future when we have seperate cudagraph capture
        # sizes for decode and mixed prefill-decode.
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and cudagraph_mode.separate_routine()
            and self.uniform_decode_query_len > 1
        ):
            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
            )
4810
4811
4812
4813
            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
4814

4815
4816
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4817
        self.compilation_config.cudagraph_mode = cudagraph_mode
4818
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4819
            cudagraph_mode, self.uniform_decode_query_len
4820
        )
4821

4822
4823
    def calculate_reorder_batch_threshold(self) -> None:
        """
4824
4825
4826
4827
        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.
4828
        """
4829
4830
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

4831
        reorder_batch_thresholds: list[int | None] = [
4832
4833
4834
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
4835
4836
4837
4838
4839
        # 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
4840
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
4841

4842
4843
4844
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4845
4846
    ) -> int:
        """
4847
4848
4849
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        Select a block size that is supported by all backends and is a factor of
        kv_manager_block_size.

        If kv_manager_block_size is supported by all backends, return it directly.
        Otherwise, return the max supported size.
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        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

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

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

        backends = [group.backend for group in attn_groups]

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

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

        Args:
            kv_cache_config: The KV cache configuration.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
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            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
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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.num_spec_tokens,
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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]:
5046
        """
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        Reshape the KV cache tensors to the desired shape and dtype.
5048

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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.
        """
5058
        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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5112
                elif isinstance(kv_cache_spec, MambaSpec):
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                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
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                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
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                            storage_offset=storage_offset_bytes // dtype_size,
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                        )
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                        state_tensors.append(tensor)
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                        storage_offset_bytes += stride[0] * dtype_size
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                    kv_caches[layer_name] = state_tensors
5136
                else:
5137
                    raise NotImplementedError
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        if has_attn and has_mamba:
5140
            self._update_hybrid_attention_mamba_layout(kv_caches)
5141

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        return kv_caches

5144
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
5147
        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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5151

        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
5157
            for layer_name in group.layer_names:
5158
                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 "
5163
                        f"a tensor of shape {kv_cache.shape}"
5164
                    )
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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:]),
                    )
5170

5171
    def initialize_kv_cache_tensors(
5172
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
5173
    ) -> 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.

5181
        Returns:
5182
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        # Try creating KV caches optimized for kv-connector transfers
        cache_dtype = self.cache_config.cache_dtype
        if self.use_uniform_kv_cache(self.attn_groups, cache_dtype):
            kv_caches, cross_layers_kv_cache, attn_backend = (
                self.allocate_uniform_kv_caches(
                    kv_cache_config,
                    self.attn_groups,
                    cache_dtype,
                    self.device,
                    kernel_block_sizes,
                )
            )
            self.cross_layers_kv_cache = cross_layers_kv_cache
            self.cross_layers_attn_backend = attn_backend
        else:
            # Fallback to the general case
            # Initialize the memory buffer for KV cache
            kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)

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

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
5247
            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:
5250
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
5253

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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
        """
5261
        kv_cache_config = deepcopy(kv_cache_config)
5262
        self.kv_cache_config = kv_cache_config
5263
        self.may_add_encoder_only_layers_to_kv_cache_config()
5264
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
5265
        self.initialize_attn_backend(kv_cache_config)
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        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
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        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

5276
        # 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
        )
5281

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        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

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Robert Shaw committed
5288
        if has_kv_transfer_group():
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            kv_transfer_group = get_kv_transfer_group()
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            if self.cross_layers_kv_cache is not None:
                assert self.cross_layers_attn_backend is not None
                kv_transfer_group.register_cross_layers_kv_cache(
                    self.cross_layers_kv_cache, self.cross_layers_attn_backend
                )
            else:
                kv_transfer_group.register_kv_caches(kv_caches)
5297
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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5298

5299
        if self.dcp_world_size > 1:
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            layer_type = cast(type[Any], AttentionLayerBase)
            layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
5302
            for layer in layers.values():
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                layer_impl = getattr(layer, "impl", None)
                if layer_impl is None:
                    continue
                assert layer_impl.need_to_return_lse_for_decode, (
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                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
5309
                    f"{layer_impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
5312

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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
5318
        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:
5322
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """
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        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        layer_type = cast(type[Any], AttentionLayerBase)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
Chen Zhang's avatar
Chen Zhang committed
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
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        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
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        return pinned.tolist()