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

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

        # Event on the copy stream so we can synchronize the non-blocking copy.
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        self.async_copy_ready_event = torch.cuda.Event()
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        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids
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        self._logprobs_tensors = logprobs_tensors
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        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
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            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
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                "cpu", non_blocking=True
            )
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            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
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            self.async_copy_ready_event.record()
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    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
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        This function blocks until the copy is finished.
        """
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        self.async_copy_ready_event.synchronize()
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        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
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        del self._sampled_token_ids

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        valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
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        for i in self._invalid_req_indices:
            valid_sampled_token_ids[i].clear()

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
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        if self._logprobs_tensors_cpu:
            # NOTE(nick): this will need to be updated to use cu_num_accepted_tokens
            # for async sched + spec decode + logprobs compatibility.
            output.logprobs = self._logprobs_tensors_cpu.tolists()
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        return output


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

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
    kv_connector_output: KVConnectorOutput | None


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class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
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        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
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        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
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        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
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        # Model-related.
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        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
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        self.hidden_size = model_config.get_hidden_size()
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        self.attention_chunk_size = model_config.attention_chunk_size
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        # Only relevant for models using ALiBi (e.g, MPT)
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        self.use_alibi = model_config.uses_alibi
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
        )
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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

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

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # None in the first PP rank. The rest are set after load_model.
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        self.intermediate_tensors: IntermediateTensors | None = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
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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(
504
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                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
506

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        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
512
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515

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

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524
        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
525

526
        self.reorder_batch_threshold: int | None = None
527

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531
532
        # 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()

533
        # Cached outputs.
534
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
535
536
        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
537
            (self.max_num_reqs, 1),
538
539
            dtype=torch.int64,
            device="cpu",
540
541
            pin_memory=self.pin_memory,
        )
542

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

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549
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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559
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
            return self.positions.gpu[num_tokens]

560
    def _make_buffer(
561
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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569
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
570

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572
573
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

574
        if not self.is_pooling_model:
575
576
            return model_kwargs

577
578
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
579
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581

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
582
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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

592
        seq_lens = self.seq_lens.gpu[:num_reqs]
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600
        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(
601
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            device=self.device
        )
603
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        return model_kwargs

605
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
606
607
        """
        Update the order of requests in the batch based on the attention
608
        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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619
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621
622
        # 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

623
        if self.reorder_batch_threshold is not None:
624
625
626
            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
627
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629
630
            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
631
                assert self.reorder_batch_threshold == 1, (
632
                    "DCP not support reorder_batch_threshold > 1 now."
633
                )
634
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636
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
637
638
                decode_threshold=self.reorder_batch_threshold,
            )
639

640
641
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
642
        """Initialize attributes from torch.cuda.get_device_properties"""
643
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649
        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()

650
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
651
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656
        """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.

657
658
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
659
660
        """
        # Remove finished requests from the cached states.
661
662
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
663
664
665
666
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668
669
        # 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:
670
            self.input_batch.remove_request(req_id)
671
672

        # Free the cached encoder outputs.
673
674
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
675

676
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680
681
682
683
684
685
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687
688
        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
689
            self.input_batch.remove_request(req_id)
690

691
        reqs_to_add: list[CachedRequestState] = []
692
        # Add new requests to the cached states.
693
694
695
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
696
            pooling_params = new_req_data.pooling_params
697

698
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700
701
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
702
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706
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

707
708
            if self.is_pooling_model:
                assert pooling_params is not None
709
710
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
711

712
                model = cast(VllmModelForPooling, self.get_model())
713
                to_update = model.pooler.get_pooling_updates(task)
714
715
                to_update.apply(pooling_params)

716
            req_state = CachedRequestState(
717
                req_id=req_id,
718
                prompt_token_ids=new_req_data.prompt_token_ids,
719
                prompt_embeds=new_req_data.prompt_embeds,
720
                mm_features=new_req_data.mm_features,
721
                sampling_params=sampling_params,
722
                pooling_params=pooling_params,
723
                generator=generator,
724
725
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
726
                output_token_ids=[],
727
                lora_request=new_req_data.lora_request,
728
            )
729
730
            self.requests[req_id] = req_state

731
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
732
            if self.uses_mrope:
733
                self._init_mrope_positions(req_state)
734

735
            reqs_to_add.append(req_state)
736

737
        # Update the states of the running/resumed requests.
738
        is_last_rank = get_pp_group().is_last_rank
739
740
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
741
            req_state = self.requests[req_id]
742
743
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
744
            resumed_from_preemption = req_id in req_data.resumed_req_ids
745
            num_output_tokens = req_data.num_output_tokens[i]
746

747
            # Update the cached states.
748

749
            req_state.num_computed_tokens = num_computed_tokens
750
            req_index = self.input_batch.req_id_to_index.get(req_id)
751
752
753
754
755
756
757
758

            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.
759
760
761
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
762
763
764
765
                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:
766
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
767
768
769
770
771
            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:
772
773
774
775
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
776
777
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
778

779
            # Update the block IDs.
780
            if not resumed_from_preemption:
781
782
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
783
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
784
                        block_ids.extend(new_ids)
785
            else:
786
                assert req_index is None
787
                assert new_block_ids is not None
788
789
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
790
                req_state.block_ids = new_block_ids
791
792
793
794
795

            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.
796
797
798
799
800
801
802

                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:]

803
                reqs_to_add.append(req_state)
804
805
806
                continue

            # Update the persistent batch.
807
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
808
            if new_block_ids is not None:
809
                self.input_batch.block_table.append_row(new_block_ids, req_index)
810
811
812
813
814
815
816

            # 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)
817
                self.input_batch.token_ids_cpu[
818
819
820
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
821
                self.input_batch.num_tokens[req_index] = end_token_index
822

823
            # Add spec_token_ids to token_ids_cpu.
824
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
825
                req_id, []
826
            )
827
828
829
830
831
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
832
833
                    req_index, start_index:end_token_index
                ] = spec_token_ids
834
835
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
836
837
838
839
840
841
842

            # When speculative decoding is used with structured output,
            # the scheduler can drop draft tokens that do not
            # conform to the schema. This can result in
            # scheduler_output.scheduled_spec_decode_tokens being empty,
            # even when speculative decoding is enabled.
            self.input_batch.spec_token_ids[req_index] = spec_token_ids
843

844
845
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
846
847
        for request in reqs_to_add:
            self.input_batch.add_request(request)
848

849
850
851
852
853
854
        # 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()
855

856
    def _update_states_after_model_execute(
857
858
        self, output_token_ids: torch.Tensor
    ) -> None:
859
860
861
862
863
864
865
866
867
868
869
870
        """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.
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
        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()
        )
891
892
893
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

894
895
896
897
898
899
    def _init_mrope_positions(self, req_state: CachedRequestState):
        image_grid_thw = []
        video_grid_thw = []
        second_per_grid_ts = []
        audio_feature_lengths = []
        use_audio_in_video = False
900
901
902
903
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
904
905
906
907
908
909
910
911
912
913
914
915
            mm_input = mm_item.get_data()
            if (t := mm_input.get("image_grid_thw")) is not None:
                image_grid_thw.append(t.tolist())
            if (t := mm_input.get("video_grid_thw")) is not None:
                video_grid_thw.append(t.tolist())
            if (t := mm_input.get("second_per_grid_ts")) is not None:
                second_per_grid_ts.append(t)
            if (t := mm_input.get("audio_feature_lengths")) is not None:
                audio_feature_lengths.append(t)
            if mm_input.get("use_audio_in_video") is True:
                use_audio_in_video = True

916
917
918
919
920
921
922
923
924
925
926
        assert supports_mrope(self.get_model()), "M-RoPE support is not implemented."

        req_state.mrope_positions, req_state.mrope_position_delta = (
            self.model.get_mrope_input_positions(
                req_state.prompt_token_ids,
                hf_config=self.model_config.hf_config,
                image_grid_thw=image_grid_thw,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
                audio_feature_lengths=audio_feature_lengths,
                use_audio_in_video=use_audio_in_video,
927
            )
928
        )
929

930
    def _extract_mm_kwargs(
931
        self,
932
933
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
934
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
935
            return {}
936

937
938
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
939
940
941
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
942

943
        # Input all modalities at once
944
        model = cast(SupportsMultiModal, self.model)
945
946
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
947
948
949
950
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
951
            multimodal_cpu_fields=model.multimodal_cpu_fields,
952
953
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
954

955
        return mm_kwargs_combined
956

957
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
958
        if not self.is_multimodal_raw_input_only_model:
959
            return {}
960

961
962
963
964
965
        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)
966

967
968
969
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
970
        cumsum_dtype: np.dtype | None = None,
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
    ) -> 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

987
988
989
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
990
        """Prepare the input IDs for the current batch.
991

992
993
994
995
996
997
998
        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)
999
1000
1001
            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)
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
1020
                indices_match &= prev_index == flattened_index
1021
1022
1023
1024
1025
1026
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1027
1028
1029
            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)
1030
1031
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1032
            # So input_ids.cpu will have all the input ids.
1033
1034
1035
1036
1037
1038
1039
            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_(
1040
1041
1042
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1043
1044
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1045
            return
1046
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1047
1048
1049
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1050
        prev_common_req_indices_tensor = torch.tensor(
1051
1052
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1053
1054
1055
1056
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1057
1058
1059
                prev_common_req_indices_tensor, 0
            ],
        )
1060

1061
1062
    def _get_encoder_seq_lens(
        self,
1063
        scheduled_encoder_inputs: dict[str, list[int]],
1064
1065
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1066
    ) -> np.ndarray | None:
1067
1068
1069
1070
1071
1072
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
            return None

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
        encoder_seq_lens = np.zeros(num_reqs, dtype=np.int32)
1073
        for req_id in scheduled_encoder_inputs:
1074
1075
1076
1077
1078
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1079
    def _prepare_inputs(
1080
1081
1082
1083
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
        max_num_scheduled_tokens: int,
1084
1085
    ) -> tuple[
        torch.Tensor,
1086
1087
1088
        SpecDecodeMetadata | None,
        UBatchSlices | None,
        torch.Tensor | None,
1089
    ]:
1090
1091
        """
        :return: tuple[
1092
            logits_indices, spec_decode_metadata,
1093
            ubatch_slices, num_tokens_across_dp,
1094
1095
        ]
        """
1096
1097
1098
1099
1100
1101
1102
        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.
1103
        self.input_batch.block_table.commit_block_table(num_reqs)
1104
1105
1106

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1107
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1108

1109
1110
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1111
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1112
1113

        # Get positions.
1114
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1115
1116
1117
1118
1119
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1120

1121
1122
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1123
        if self.uses_mrope:
1124
1125
            self._calc_mrope_positions(scheduler_output)

1126
1127
1128
1129
        # 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.
1130
1131
1132
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1133
        token_indices_tensor = torch.from_numpy(token_indices)
1134

1135
1136
1137
        # 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.
1138
1139
1140
1141
1142
1143
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1144
        if self.enable_prompt_embeds:
1145
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1146
1147
1148
1149
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1150
1151
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184

        # 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:
1185
1186
1187
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1188
1189

                output_idx += num_sched
1190

1191
1192
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1193
1194

        # Prepare the attention metadata.
1195
        self.query_start_loc.np[0] = 0
1196
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1197
1198
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1199
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1200
        self.query_start_loc.copy_to_gpu()
1201
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1202

1203
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1204
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1205
1206
1207
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1208
1209
1210
1211
1212
1213
1214

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

1215
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1216
1217
1218
1219
1220
1221
1222
            num_tokens_unpadded=num_tokens_unpadded,
            parallel_config=self.parallel_config,
            allow_microbatching=True,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=num_tokens_padded,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
1223
        )
1224

1225
        self.seq_lens.np[:num_reqs] = (
1226
1227
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1228
        # Fill unused with 0 for full cuda graph mode.
1229
1230
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1231

1232
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1233
1234
1235
1236
1237
1238
1239
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

        # Record the index of requests that should not be sampled,
        # so that we could clear the sampled tokens before returning
        discard_requests_mask = self.seq_lens.np[:num_reqs] < num_tokens_np
        discard_request_indices = np.nonzero(discard_requests_mask)[0]
        self.num_discarded_requests = len(discard_request_indices)
1240
1241
1242
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1243
1244
1245

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1246
        # Copy the tensors to the GPU.
1247
1248
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1249
        if self.uses_mrope:
1250
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1251
1252
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1253
1254
                non_blocking=True,
            )
1255
1256
        else:
            # Common case (1D positions)
1257
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1258

1259
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1260
1261
1262
1263
1264
1265
1266
        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
1267
            num_draft_tokens = None
1268
            spec_decode_metadata = None
1269
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1270
1271
1272
1273
1274
        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)
1275
1276
1277
            # 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)
1278
1279
1280
1281
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1282
1283
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1284
1285
1286
1287
1288
1289
1290
1291
                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
                )
1292
            spec_decode_metadata = self._calc_spec_decode_metadata(
1293
1294
                num_draft_tokens, cu_num_tokens
            )
1295
            logits_indices = spec_decode_metadata.logits_indices
1296
            num_sampled_tokens = num_draft_tokens + 1
1297
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1298
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1299
1300
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1301

1302
1303
1304
1305
1306
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1307
            )
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
            ubatch_slices,
            num_tokens_across_dp,
        )

    def _build_attention_metadata(
        self,
        total_num_scheduled_tokens: int,
        max_num_scheduled_tokens: int,
        num_reqs: int,
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
        scheduled_encoder_inputs: dict[str, list[int]] | None = None,
        cascade_attn_prefix_lens: list[list[int]] | None = None,
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
        logits_indices_padded = None
        num_logits_indices = 0
        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
                )
1342

1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
        # update seq_lens of decode reqs under DCP.
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
                self.seq_lens.cpu[:num_reqs],
                self.dcp_world_size,
                self.dcp_rank,
                self.parallel_config.dcp_kv_cache_interleave_size,
            )
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs)

1353
1354
1355
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1356

1357
1358
        # Used in the below loop
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1359
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1360
        seq_lens = self.seq_lens.gpu[:num_reqs]
1361
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1362
1363
1364
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1365
1366
1367
        dcp_local_seq_lens = (
            self.dcp_local_seq_lens.gpu[:num_reqs] if self.dcp_world_size > 1 else None
        )
1368
        spec_decode_common_attn_metadata = None
1369
1370
1371
1372
1373
1374
1375
1376
1377

        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()

1378
1379
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1380
1381
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1382
1383
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1384

1385
1386
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1387
        for kv_cache_gid, kv_cache_group in enumerate(
1388
1389
            self.kv_cache_config.kv_cache_groups
        ):
1390
            encoder_seq_lens = self._get_encoder_seq_lens(
1391
1392
1393
                scheduled_encoder_inputs or {},
                kv_cache_group.kv_cache_spec,
                num_reqs,
1394
            )
1395

1396
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1397
1398
1399
1400
1401
                # Encoder-only layers do not have KV cache, so we need to
                # create a dummy block table and slot mapping for them.
                blk_table_tensor = torch.zeros(
                    (num_reqs, 1),
                    dtype=torch.int32,
1402
1403
1404
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1405
                    (total_num_scheduled_tokens,),
1406
1407
1408
                    dtype=torch.int64,
                    device=self.device,
                )
1409
            else:
1410
                blk_table = self.input_batch.block_table[kv_cache_gid]
1411
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1412
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1413
1414
1415

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1416
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
1417

1418
            common_attn_metadata = CommonAttentionMetadata(
1419
1420
1421
1422
1423
                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,
1424
1425
1426
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1427
                max_seq_len=max_seq_len,
1428
1429
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1430
                logits_indices_padded=logits_indices_padded,
1431
                num_logits_indices=num_logits_indices,
1432
                causal=True,
1433
                encoder_seq_lens=encoder_seq_lens,
1434
                dcp_local_seq_lens=dcp_local_seq_lens,
1435
1436
            )

1437
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1438
                if isinstance(self.drafter, EagleProposer):
1439
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1440
1441
1442
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1443

1444
1445
1446
1447
1448
1449
            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
                )
1450
                builder = attn_group.get_metadata_builder()
1451

1452
                extra_attn_metadata_args = {}
1453
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1454
                    extra_attn_metadata_args = dict(
1455
1456
1457
1458
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1459
1460
                    )

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1462
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1463
1464
                        ubatch_slices, common_attn_metadata
                    )
1465
                    for ubid, common_attn_metadata in enumerate(
1466
1467
                        common_attn_metadata_list
                    ):
1468
1469
1470
1471
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1473
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1475
1476
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1478
                        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:
1479
1480
1481
1482
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
                    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,
                        )
1493
1494
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1495

1496
        return attn_metadata, spec_decode_common_attn_metadata
1497

1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: list[int],
    ) -> list[list[int]] | None:
        """
        :return: Optional[cascade_attn_prefix_lens]
            cascade_attn_prefix_lens is 2D: ``[kv_cache_group_id][attn_group_idx]``,
            None if we should not use cascade attention
        """
1508

1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
        use_cascade_attn = False
        num_kv_cache_groups = len(self.kv_cache_config.kv_cache_groups)
        cascade_attn_prefix_lens: list[list[int]] = [
            [] for _ in range(num_kv_cache_groups)
        ]

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

        return cascade_attn_prefix_lens if use_cascade_attn else None
1531

1532
1533
1534
1535
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1536
1537
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
    ) -> 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.
        """
1556

1557
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
        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]
1595
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1596
1597
1598
1599
1600
1601
1602
        # If we use [A, B, C, D] as the common prefix for Request 1-3,
        # then Request 3 will be processed only by the first kernel,
        # and the second kernel will get an empty input. While this is not
        # a fundamental problem, our current implementation does not support
        # this case.
        num_reqs = len(num_scheduled_tokens)
        common_prefix_len = min(
1603
1604
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1605
        # common_prefix_len should be a multiple of the block size.
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
        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
        )
1617
1618
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1619
1620
1621
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1622
            num_kv_heads=kv_cache_spec.num_kv_heads,
1623
            use_alibi=self.use_alibi,
1624
            use_sliding_window=use_sliding_window,
1625
            use_local_attention=use_local_attention,
1626
            num_sms=self.num_sms,
1627
            dcp_world_size=self.dcp_world_size,
1628
1629
1630
        )
        return common_prefix_len if use_cascade else 0

1631
1632
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1633
        for index, req_id in enumerate(self.input_batch.req_ids):
1634
1635
1636
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1637
1638
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1639
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1640
1641
                req.prompt_token_ids, req.prompt_embeds
            )
1642
1643

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1644
1645
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
            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

1659
1660
1661
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1662
1663
1664
1665
1666
1667
1668
                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

1669
                MRotaryEmbedding.get_next_input_positions_tensor(
1670
                    out=self.mrope_positions.np,
1671
1672
1673
1674
1675
                    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,
                )
1676
1677
1678

                mrope_pos_ptr += completion_part_len

1679
1680
    def _calc_spec_decode_metadata(
        self,
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
        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
1697
1698
1699
1700

        # 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(
1701
1702
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1703
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1704
        logits_indices = np.repeat(
1705
1706
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1707
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1708
1709
1710
1711
1712
1713
        logits_indices += arange

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

        # Compute the draft logits indices.
1714
1715
1716
        # 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(
1717
1718
            num_draft_tokens, cumsum_dtype=np.int32
        )
1719
1720
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1721
1722
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1723
1724
1725
1726
1727
        # [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(
1728
1729
            self.device, non_blocking=True
        )
1730
1731
1732
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1733
1734
1735
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1736
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1737
1738
            self.device, non_blocking=True
        )
1739
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1740
1741
            self.device, non_blocking=True
        )
1742

1743
1744
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1745
        draft_token_ids = self.input_ids.gpu[logits_indices]
1746
1747
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1748
        return SpecDecodeMetadata(
1749
1750
1751
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1752
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1753
1754
1755
1756
1757
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1758
1759
1760
1761
1762
1763
1764
    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
1765
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1766
1767
1768
1769
1770
        # 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_(
1771
1772
1773
1774
1775
1776
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1777
1778
1779
1780
1781
            # 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
1782
1783
1784
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1785
1786
        return logits_indices_padded

1787
1788
1789
1790
1791
1792
1793
1794
    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
1795
                inputs.
1796
1797
1798
1799
1800
1801

        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
        """
1802
1803
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1804
            return [], []
1805
        # Batch the multi-modal inputs.
1806
        mm_kwargs = list[MultiModalKwargsItem]()
1807
1808
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1809
1810
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1811
1812

            for mm_input_id in encoder_input_ids:
1813
1814
1815
1816
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1817

1818
1819
1820
1821
1822
        return mm_kwargs, mm_hashes_pos

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1823
1824
            scheduler_output
        )
1825
1826
1827
1828

        if not mm_kwargs:
            return

1829
1830
1831
1832
1833
1834
1835
        # 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.
1836
        model = cast(SupportsMultiModal, self.model)
1837
        encoder_outputs = []
1838
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1839
1840
1841
1842
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1843
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1844
        ):
1845
1846
1847
            curr_group_outputs = []

            # EVS-related change.
1848
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1849
            # processing multimodal data. This solves the issue with scheduler
1850
1851
1852
1853
            # 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)
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
            # 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,
1870
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
1871
                        )
1872
                    )
1873
1874

                    micro_batch_outputs = model.get_multimodal_embeddings(
1875
1876
                        **micro_batch_mm_inputs
                    )
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886

                    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.
1887
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1888

1889
1890
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1891
                expected_num_items=num_items,
1892
            )
1893
            encoder_outputs.extend(curr_group_outputs)
1894

1895
1896
1897
        # 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(
1898
1899
1900
1901
1902
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1903
1904
        self,
        scheduler_output: "SchedulerOutput",
1905
        shift_computed_tokens: int = 0,
1906
1907
1908
1909
1910
1911
1912
1913
    ) -> 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
1914
        should_sync_mrope_positions = False
1915

1916
        for req_id in self.input_batch.req_ids:
1917
1918
            mm_embeds_req: list[torch.Tensor] = []

1919
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1920
            req_state = self.requests[req_id]
1921
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1922

1923
1924
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1925
1926
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942

                # 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,
1943
1944
                    num_encoder_tokens,
                )
1945
                assert start_idx < end_idx
1946

1947
                mm_hash = mm_feature.identifier
1948
                encoder_output = self.encoder_cache.get(mm_hash, None)
1949
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1950
1951
1952
1953

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

1954
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1955
1956
1957
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1958

1959
1960
1961
1962
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1963
1964
1965
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1966
                assert req_state.mrope_positions is not None
1967
1968
1969
1970
1971
1972
1973
                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,
1974
1975
                    )
                )
1976
1977
1978
1979
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1980
1981
1982
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1983
1984
1985

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1986
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1987

1988
        return mm_embeds, is_mm_embed
1989

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
    def _extract_encoder_inputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> dict[str, torch.Tensor]:
        """Extract encoder inputs for encoder-decoder models.

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

        if not mm_kwargs:
            return {}

        # Group MM kwargs by modality and extract features
2006
        model = cast(SupportsMultiModal, self.model)
2007
2008
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
2009
2010
2011
2012
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
2013
            multimodal_cpu_fields=model.multimodal_cpu_fields,
2014
2015
2016
2017
2018
2019
2020
2021
        ):
            # Add the grouped features to encoder_features dict
            # This allows the model to receive them as kwargs (e.g.,
            # input_features=...)
            encoder_features.update(mm_kwargs_group)

        return encoder_features

2022
    def get_model(self) -> nn.Module:
2023
        # get raw model out of the cudagraph wrapper.
2024
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2025
            return self.model.unwrap()
2026
2027
        return self.model

2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
    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

2043
2044
2045
2046
2047
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2048
2049
        supported_tasks = list(model.pooler.get_supported_tasks())

2050
2051
2052
2053
2054
        if self.scheduler_config.chunked_prefill_enabled:
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2055

2056
2057
            logger.debug_once(
                "Chunked prefill is not supported with "
2058
2059
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2060
2061
2062
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2063
2064
2065
2066
2067

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2068
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2069
2070

        return supported_tasks
2071

2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
    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)

2082
    def sync_and_slice_intermediate_tensors(
2083
2084
2085
2086
2087
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
2088
2089
2090
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2091
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2092
2093
2094
2095
2096
2097

        # 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():
2098
                is_scattered = k == "residual" and is_rs
2099
                copy_len = num_tokens // tp if is_scattered else num_tokens
2100
                self.intermediate_tensors[k][:copy_len].copy_(
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
                    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:
2114
2115
2116
2117
2118
2119
2120
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2121
2122
        model = self.get_model()
        assert is_mixture_of_experts(model)
2123
2124
2125
        self.eplb_state.step(
            is_dummy,
            is_profile,
2126
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2127
2128
        )

2129
2130
2131
2132
    # This is where the second ubatch is adjusted to account for the padding.
    # Should be called after attention metadata creation. This just pads
    # the second ubatch slice out to the total number of tokens
    # (num_tokens + padding)
2133
2134
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2135
2136
2137
2138
2139
2140
        padded_second_ubatch_slice = slice(
            ubatch_slices[1].token_slice.start, num_total_tokens
        )
        ubatch_slices[1] = UBatchSlice(
            padded_second_ubatch_slice, padded_second_ubatch_slice
        )
2141

2142
2143
2144
2145
2146
2147
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2148
2149
2150
        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"
        )
2151

2152
        hidden_states = hidden_states[:num_scheduled_tokens]
2153
        pooling_metadata = self.input_batch.get_pooling_metadata()
2154
2155
2156
2157
        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]
2158

2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
        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()
2169

2170
        pooler_output: list[torch.Tensor | None] = []
2171
        for raw_output, seq_len, prompt_len in zip(
2172
2173
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2174
            output = raw_output if seq_len == prompt_len else None
2175
            pooler_output.append(output)
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185

        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,
        )

2186
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2187
2188
2189
2190
2191
2192
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2193
2194
2195
2196
2197
2198
2199
2200
            # Use CUDA graphs.
            # Add padding to the batch size.
            return self.vllm_config.pad_for_cudagraph(num_scheduled_tokens)

        # Eager mode.
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2201
2202
2203
2204
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2205
2206
2207
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2208
    def _preprocess(
2209
2210
        self,
        scheduler_output: "SchedulerOutput",
2211
        num_input_tokens: int,  # Padded
2212
        intermediate_tensors: IntermediateTensors | None = None,
2213
    ) -> tuple[
2214
2215
        torch.Tensor | None,
        torch.Tensor | None,
2216
        torch.Tensor,
2217
        IntermediateTensors | None,
2218
2219
        dict[str, Any],
    ]:
2220
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2221
        is_first_rank = get_pp_group().is_first_rank
2222

2223
2224
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2225
2226
        if (
            self.supports_mm_inputs
2227
            and is_first_rank
2228
2229
            and not self.model_config.is_encoder_decoder
        ):
2230
2231
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2232
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2233

2234
2235
2236
            # 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.
2237
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2238
2239
2240
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2241
            )
2242

2243
            # TODO(woosuk): Avoid the copy. Optimize.
2244
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2245

2246
            input_ids = None
2247
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2248
2249
2250
2251
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2252
        elif self.enable_prompt_embeds and is_first_rank:
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
            # 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).
2265
2266
2267
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2268
                .squeeze(1)
2269
            )
2270
2271
2272
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2273
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2274
2275
2276
2277
2278
                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
2279
        else:
2280
2281
2282
2283
            # 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.
2284
            input_ids = self.input_ids.gpu[:num_input_tokens]
2285
            inputs_embeds = None
2286
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2287
        if self.uses_mrope:
2288
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2289
        else:
2290
            positions = self.positions.gpu[:num_input_tokens]
2291

2292
        if is_first_rank:
2293
2294
            intermediate_tensors = None
        else:
2295
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2296
2297
                num_input_tokens, intermediate_tensors, True
            )
2298

2299
2300
2301
2302
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2303
2304
2305
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2306
2307
2308
2309
2310
2311
2312
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2313

2314
    def _sample(
2315
        self,
2316
2317
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2318
    ) -> SamplerOutput:
2319
        # Sample the next token and get logprobs if needed.
2320
        sampling_metadata = self.input_batch.sampling_metadata
2321
        if spec_decode_metadata is None:
2322
2323
2324
            # 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()
2325
            return self.sampler(
2326
2327
2328
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2329

2330
        sampler_output = self.rejection_sampler(
2331
2332
            spec_decode_metadata,
            None,  # draft_probs
2333
            logits,
2334
2335
            sampling_metadata,
        )
2336
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2337
2338
2339
        return sampler_output

    def _bookkeeping_sync(
2340
2341
2342
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2343
        logits: torch.Tensor | None,
2344
2345
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2346
        spec_decode_metadata: SpecDecodeMetadata | None,
2347
    ) -> tuple[
2348
        dict[str, int],
2349
        LogprobsLists | None,
2350
        list[list[int]],
2351
        dict[str, LogprobsTensors | None],
2352
2353
2354
        list[str],
        dict[str, int],
        list[int],
2355
    ]:
2356
2357
2358
2359
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2360
2361
2362
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2363
2364
2365
2366
        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)
2367

2368
2369
2370
        # 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()
2371
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2372
2373

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2374
        sampled_token_ids = sampler_output.sampled_token_ids
2375
        invalid_req_indices = []
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
        if not self.use_async_scheduling:
            # Get the valid generated tokens.
            max_gen_len = sampled_token_ids.shape[-1]
            if max_gen_len == 1:
                # No spec decode tokens.
                valid_sampled_token_ids = self._to_list(sampled_token_ids)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
2390
                valid_sampled_token_ids[int(i)].clear()
2391
        else:
2392
            valid_sampled_token_ids = []
2393
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2394
2395
2396
2397
2398
2399
            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
2400
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2401
2402
2403
2404
2405
            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
            }
2406

2407
2408
2409
2410
2411
        # 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.
2412
        req_ids = self.input_batch.req_ids
2413
2414
2415
2416
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2417
2418
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2419
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2420
2421
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2422
2423
2424
2425
2426
2427
2428
2429

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

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

2430
2431
2432
2433
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2434
            end_idx = start_idx + num_sampled_ids
2435
2436
2437
2438
            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}"
2439
            )
2440

2441
2442
            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
2443
2444
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2445

2446
            req_id = req_ids[req_idx]
2447
2448
2449
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2450
2451
        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
2452
            if not self.use_async_scheduling and logprobs_tensors is not None
2453
2454
2455
2456
2457
2458
2459
2460
2461
            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,
        )

2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
        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,
        )

2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
    @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()

2487
2488
    def _model_forward(
        self,
2489
2490
2491
2492
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2493
2494
2495
2496
2497
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2498
        Motivation: We can inspect only this method versus
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
        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,
        )

2519
2520
2521
2522
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2523
        intermediate_tensors: IntermediateTensors | None = None,
2524
2525
2526
2527
2528
2529
2530
    ) -> ModelRunnerOutput | IntermediateTensors | None:
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2531
        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
2532
2533
2534
2535
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

2536
                if not num_scheduled_tokens:
2537
2538
2539
2540
                    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(
2541
2542
                        scheduler_output, self.vllm_config
                    )
2543
2544
2545
2546
                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2547
2548
                        "it when the requests need prompt logprobs"
                    )
2549

2550
2551
2552
2553
2554
2555
                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())

2556
2557
2558
2559
                (
                    logits_indices,
                    spec_decode_metadata,
                    ubatch_slices,
2560
                    num_tokens_across_dp,
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
                ) = self._prepare_inputs(
                    scheduler_output, num_scheduled_tokens_np, max_num_scheduled_tokens
                )

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

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

                total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
                use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
                attn_metadata, spec_decode_common_attn_metadata = (
                    self._build_attention_metadata(
                        total_num_scheduled_tokens=total_num_scheduled_tokens,
                        max_num_scheduled_tokens=max_num_scheduled_tokens,
                        num_reqs=num_reqs,
                        ubatch_slices=ubatch_slices,
                        logits_indices=logits_indices,
                        use_spec_decode=use_spec_decode,
                        scheduled_encoder_inputs=scheduler_output.scheduled_encoder_inputs,
                        cascade_attn_prefix_lens=cascade_attn_prefix_lens,
                    )
                )
2592

2593
            dp_rank = self.parallel_config.data_parallel_rank
2594
2595
            if ubatch_slices:
                assert num_tokens_across_dp is not None
2596
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
2597
2598
                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
2599
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
2600
2601
2602
2603
2604
            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

2605
2606
2607
2608
2609
2610
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
2611
            ) = self._preprocess(
2612
                scheduler_output, num_input_tokens, intermediate_tensors
2613
2614
            )

2615
2616
2617
            uniform_decode = (
                max_num_scheduled_tokens == self.uniform_decode_query_len
            ) and (num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
2618
            batch_descriptor = BatchDescriptor(
2619
2620
2621
                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
2622
2623
            )
            cudagraph_runtime_mode, batch_descriptor = (
2624
2625
2626
2627
                self.cudagraph_dispatcher.dispatch(
                    batch_descriptor,
                    use_cascade_attn=cascade_attn_prefix_lens is not None,
                )
2628
            )
2629

2630
        # Set cudagraph mode to none if calc_kv_scales is true.
2631
2632
2633
2634
2635
2636
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
            cudagraph_runtime_mode = CUDAGraphMode.NONE
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
2637

2638
2639
        # Run the model.
        # Use persistent buffers for CUDA graphs.
2640
2641
        with (
            set_forward_context(
2642
2643
2644
2645
2646
2647
                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                batch_descriptor=batch_descriptor,
2648
                ubatch_slices=ubatch_slices,
2649
            ),
2650
            record_function_or_nullcontext("gpu_model_runner: forward"),
2651
2652
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2653
            model_output = self._model_forward(
2654
2655
2656
2657
2658
2659
2660
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

2661
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
2662
            if self.use_aux_hidden_state_outputs:
2663
                # True when EAGLE 3 is used.
2664
2665
                hidden_states, aux_hidden_states = model_output
            else:
2666
                # Common case.
2667
2668
2669
                hidden_states = model_output
                aux_hidden_states = None

2670
2671
2672
2673
2674
            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)
2675
2676
                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
2677

2678
                if self.is_pooling_model:
2679
                    # Return the pooling output.
2680
2681
2682
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
2683
2684
                    output.kv_connector_output = kv_connector_output
                    return output
2685
2686

                sample_hidden_states = hidden_states[logits_indices]
2687
                logits = self.model.compute_logits(sample_hidden_states)
2688
2689
2690
2691
            else:
                # Rare case.
                assert not self.is_pooling_model

2692
                sample_hidden_states = hidden_states[logits_indices]
2693
                if not get_pp_group().is_last_rank:
2694
                    all_gather_tensors = {
2695
2696
2697
                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2698
                    }
2699
                    get_pp_group().send_tensor_dict(
2700
2701
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
2702
2703
                        all_gather_tensors=all_gather_tensors,
                    )
2704
2705
                    logits = None
                else:
2706
                    logits = self.model.compute_logits(sample_hidden_states)
2707
2708
2709
2710
2711

                model_output_broadcast_data = {}
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

2712
2713
2714
                model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
2715
2716
2717
                assert model_output_broadcast_data is not None
                logits = model_output_broadcast_data["logits"]

2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
            kv_connector_output,
        )
        return None

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

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
            kv_connector_output,
        ) = 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
            )
2757

2758
        with record_function_or_nullcontext("gpu_model_runner: sample"):
2759
2760
            sampler_output = self._sample(logits, spec_decode_metadata)

2761
2762
        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
2763
            with record_function_or_nullcontext("gpu_model_runner: draft"):
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
                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,
                )

2775
2776
2777
2778
2779
        use_padded_batch_for_eagle = (
            self.speculative_config
            and self.speculative_config.use_eagle()
            and not self.speculative_config.disable_padded_drafter_batch
        )
2780
2781
2782
        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
2783
2784
2785
2786
2787
        if (
            self.speculative_config
            and self.speculative_config.draft_model_config is not None
            and self.speculative_config.draft_model_config.max_model_len is not None
        ):
2788
            effective_drafter_max_model_len = (
2789
2790
                self.speculative_config.draft_model_config.max_model_len
            )
2791
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
2792
2793
2794
2795
            spec_decode_common_attn_metadata.max_seq_len
            + self.speculative_config.num_speculative_tokens
            <= effective_drafter_max_model_len
        )
2796
        if use_padded_batch_for_eagle and input_fits_in_drafter:
2797
2798
2799
2800
            # EAGLE speculative decoding can use the GPU sampled tokens
            # as inputs, and does not need to wait for bookkeeping to finish.
            propose_draft_token_ids(sampler_output.sampled_token_ids)

2801
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
2802
2803
2804
2805
2806
2807
2808
2809
            (
                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,
2810
2811
2812
2813
2814
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
2815
                scheduler_output.total_num_scheduled_tokens,
2816
                spec_decode_metadata,
2817
            )
2818

2819
2820
2821
2822
2823
        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
2824
2825
2826
            # 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)
2827

2828
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
2829
            self.eplb_step()
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
        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,
                num_nans_in_logits=num_nans_in_logits,
            )
2841

2842
2843
        if not self.use_async_scheduling:
            return output
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
                async_output_copy_stream=self.async_output_copy_stream,
            )
        with record_function_or_nullcontext(
            "gpu_model_runner: set_async_sampled_token_ids"
        ):
            # Save ref of sampled_token_ids CPU tensor if the batch contains
            # any requests with sampling params that that require output ids.
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
2863
2864
2865

        return async_output

2866
    def take_draft_token_ids(self) -> DraftTokenIds | None:
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
        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)

2877
2878
2879
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2880
        sampled_token_ids: torch.Tensor | list[list[int]],
2881
2882
2883
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
2884
2885
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2886
        common_attn_metadata: CommonAttentionMetadata,
2887
    ) -> list[list[int]] | torch.Tensor:
2888
2889
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2890
            assert isinstance(sampled_token_ids, list)
2891
            assert isinstance(self.drafter, NgramProposer)
2892
            draft_token_ids = self.drafter.propose(
2893
2894
                sampled_token_ids,
                self.input_batch.req_ids,
2895
2896
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2897
2898
                self.input_batch.spec_decode_unsupported_reqs,
            )
2899
2900
2901
2902
        elif self.speculative_config.method == "suffix":
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
2903
        elif self.speculative_config.method == "medusa":
2904
            assert isinstance(sampled_token_ids, list)
2905
            assert isinstance(self.drafter, MedusaProposer)
2906

2907
2908
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2909
2910
2911
2912
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
2913
2914
2915
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
2916
                for num_draft, tokens in zip(
2917
2918
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
2919
2920
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2921
                indices = torch.tensor(indices, device=self.device)
2922
2923
                hidden_states = sample_hidden_states[indices]

2924
            draft_token_ids = self.drafter.propose(
2925
2926
2927
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2928
        elif self.speculative_config.use_eagle():
2929
            assert isinstance(self.drafter, EagleProposer)
2930
2931
2932
2933
2934

            if self.speculative_config.disable_padded_drafter_batch:
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
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                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2937
                    "padded-batch is disabled."
2938
                )
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                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
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                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
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            else:
                # When using padded-batch, the sampled_token_ids should be
                # the gpu tensor of sampled tokens for each request, of shape
                # (num_reqs, num_spec_tokens + 1) with rejected tokens having
                # value -1.
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                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
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                    "padded-batch is enabled."
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                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
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                        self.num_discarded_requests,
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                    )
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                )
Jiayi Yao's avatar
Jiayi Yao committed
2964

2965
            if spec_decode_metadata is None:
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                token_indices_to_sample = None
2967
                # input_ids can be None for multimodal models.
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                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
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                target_positions = self._get_positions(num_scheduled_tokens)
2970
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
2971
                    assert aux_hidden_states is not None
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                    target_hidden_states = torch.cat(
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                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
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            else:
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                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
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                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
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                else:
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                    common_attn_metadata, token_indices, token_indices_to_sample = (
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                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
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                            valid_sampled_tokens_count,
                        )
                    )
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                target_token_ids = self.input_ids.gpu[token_indices]
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                target_positions = self._get_positions(token_indices)
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                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
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                    assert aux_hidden_states is not None
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                    target_hidden_states = torch.cat(
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                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            if self.supports_mm_inputs:
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
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            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
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                last_token_indices=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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                mm_embed_inputs=mm_embed_inputs,
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            )
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        return draft_token_ids
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    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
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            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
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                f"Allowed configs: {allowed_config_names}"
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            )
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

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    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
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        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
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        global_expert_loads, old_global_expert_indices_per_model, rank_mapping = (
            EplbState.get_eep_state(self.parallel_config)
            if eep_scale_up
            else (None, None, None)
        )
3051

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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
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        with DeviceMemoryProfiler() as m:
3056
            time_before_load = time.perf_counter()
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            model_loader = get_model_loader(self.load_config)
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            self.model = model_loader.load_model(
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                vllm_config=self.vllm_config, model_config=self.model_config
            )
3061
            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3065
            if hasattr(self, "drafter"):
3066
                logger.info_once("Loading drafter model...")
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                self.drafter.load_model(self.model)
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                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
                        self.vllm_config.speculative_config.draft_model_config.model,
                    )

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

3099
            if self.use_aux_hidden_state_outputs:
3100
                if not supports_eagle3(self.get_model()):
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                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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                        "aux_hidden_state_outputs was requested"
                    )
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3117

                # 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)
3118
            time_after_load = time.perf_counter()
3119
        self.model_memory_usage = m.consumed_memory
3120
        logger.info_once(
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            "Model loading took %.4f GiB memory and %.6f seconds",
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3123
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3124
            scope="local",
3125
        )
3126
        prepare_communication_buffer_for_model(self.model)
3127
        self.is_multimodal_pruning_enabled = (
3128
            supports_multimodal_pruning(self.get_model())
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3130
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
3131

3132
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
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            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            global_expert_load = (
                global_expert_loads[eplb_models] if global_expert_loads else None
            )
            old_global_expert_indices = (
                old_global_expert_indices_per_model[eplb_models]
                if old_global_expert_indices_per_model
                else None
            )
            assert self.eplb_state is not None
            self.eplb_state.add_model(
3144
                self.model,
3145
                self.model_config,
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3148
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
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3150
            )

3151
        if (
3152
3153
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3154
            and supports_dynamo()
3155
        ):
3156
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3157
            compilation_counter.stock_torch_compile_count += 1
3158
            self.model.compile(fullgraph=True, backend=backend)
3159
            return
3160
        # for other compilation modes, cudagraph behavior is controlled by
3161
3162
3163
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
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3167
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3170
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
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3172
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3173
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3176
            else:
3177
3178
3179
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3180

3181
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3182
3183
3184
3185
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        """Extract Eagle3 auxiliary layer indices from speculative config.

        These indices specify which hidden states from the base model should
        be used as auxiliary inputs for the Eagle3 drafter model during
        speculative decoding.

        Returns:
            Tuple of layer indices if found in draft model config,
            None otherwise.
        """
        if not (self.speculative_config and self.speculative_config.draft_model_config):
            return None

        hf_config = self.speculative_config.draft_model_config.hf_config
        if not hasattr(hf_config, "eagle_aux_hidden_state_layer_ids"):
            return None

        layer_ids = hf_config.eagle_aux_hidden_state_layer_ids
        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

3205
    def reload_weights(self) -> None:
3206
        assert getattr(self, "model", None) is not None, (
3207
            "Cannot reload weights before model is loaded."
3208
        )
3209
3210
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3211
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3212

3213
3214
3215
3216
3217
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3218
            self.get_model(),
3219
            tensorizer_config=tensorizer_config,
3220
            model_config=self.model_config,
3221
3222
        )

3223
3224
3225
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3226
        num_scheduled_tokens: dict[str, int],
3227
    ) -> dict[str, LogprobsTensors | None]:
3228
3229
3230
3231
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3232
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3233
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3234
3235
3236
3237
3238

        # 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():
3239
            num_tokens = num_scheduled_tokens[req_id]
3240
3241
3242

            # Get metadata for this request.
            request = self.requests[req_id]
3243
3244
3245
3246
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3247
3248
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3249
3250
                self.device, non_blocking=True
            )
3251

3252
3253
3254
3255
3256
3257
            # 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(
3258
3259
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3260
3261
                in_progress_dict[req_id] = logprobs_tensors

3262
            # Determine number of logits to retrieve.
3263
3264
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3265
            num_remaining_tokens = num_prompt_tokens - start_tok
3266
            if num_tokens <= num_remaining_tokens:
3267
                # This is a chunk, more tokens remain.
3268
3269
3270
                # 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.
3271
3272
3273
3274
3275
                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)
3276
3277
3278
3279
3280
3281
3282
                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
3283
3284
3285
3286
3287

            # 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]
3288
            offset = self.query_start_loc.np[req_idx].item()
3289
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3290
            logits = self.model.compute_logits(prompt_hidden_states)
3291
3292
3293
3294

            # 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.
3295
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3296
3297

            # Compute prompt logprobs.
3298
3299
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3300
3301
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3302
3303

            # Transfer GPU->CPU async.
3304
3305
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3306
3307
3308
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3309
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3310
3311
                ranks, non_blocking=True
            )
3312
3313
3314
3315
3316

        # 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]
3317
            del in_progress_dict[req_id]
3318
3319

        # Must synchronize the non-blocking GPU->CPU transfers.
3320
        if prompt_logprobs_dict:
3321
            self._sync_device()
3322
3323
3324

        return prompt_logprobs_dict

3325
3326
    def _get_nans_in_logits(
        self,
3327
        logits: torch.Tensor | None,
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
    ) -> 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])
3339
3340
3341
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3342
3343
3344
3345
            return num_nans_in_logits
        except IndexError:
            return {}

3346
3347
3348
3349
3350
3351
    @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
3352
         - during DP rank dummy run
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
        """
        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(
3364
                    self.input_ids.gpu,
3365
3366
                    low=0,
                    high=self.model_config.get_vocab_size(),
3367
3368
                    dtype=input_ids.dtype,
                )
3369

3370
            logger.debug_once("Randomizing dummy data for DP Rank")
3371
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3372
3373
3374
            yield
            input_ids.fill_(0)

3375
3376
3377
3378
3379
3380
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3381
3382
        assert self.mm_budget is not None

3383
3384
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3385
            seq_len=self.max_model_len,
3386
            mm_counts={modality: 1},
3387
            cache=self.mm_budget.cache,
3388
3389
3390
3391
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3392
3393
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3394

3395
        model = cast(SupportsMultiModal, self.model)
3396
3397
3398
3399
3400
3401
3402
        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,
3403
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3404
3405
            )
        )
3406

3407
3408
3409
3410
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3411
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3412
3413
        force_attention: bool = False,
        uniform_decode: bool = False,
3414
        allow_microbatching: bool = True,
3415
3416
        skip_eplb: bool = False,
        is_profile: bool = False,
3417
        create_mixed_batch: bool = False,
3418
        remove_lora: bool = True,
3419
        activate_lora: bool = False,
3420
    ) -> tuple[torch.Tensor, torch.Tensor]:
3421
3422
3423
3424
3425
3426
3427
        """
        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.
3428
                - if not set will determine the cudagraph mode based on using
3429
                    the self.cudagraph_dispatcher.
3430
3431
3432
3433
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3434
            force_attention: If True, always create attention metadata. Used to
3435
3436
3437
3438
                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.
3439
3440
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3441
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3442
            activate_lora: If False, dummy_run is performed without LoRAs.
3443
        """
3444
3445
3446
3447
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3448

3449
        # If cudagraph_mode.decode_mode() == FULL and
3450
        # cudagraph_mode.separate_routine(). This means that we are using
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
        # 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.
3462
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3463

3464
3465
3466
3467
3468
        # 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
3469
3470
3471
3472
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3473
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3474
3475
3476
3477
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3478
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3479
3480
3481
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3482
            assert not create_mixed_batch
3483
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3484
3485
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3486
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3487
3488
3489
3490
3491
3492
        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

3493
3494
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3495
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3496
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3497
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3498

3499
3500
3501
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3502
3503
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3504
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3505
3506
3507
3508
3509
3510
3511
            num_tokens_unpadded=total_num_scheduled_tokens,
            parallel_config=self.vllm_config.parallel_config,
            allow_microbatching=allow_microbatching,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=total_num_scheduled_tokens,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
3512
3513
3514
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3515
3516
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3517

3518
        attn_metadata: PerLayerAttnMetadata | None = None
3519
3520
3521

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3522
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3523
3524
3525
3526
3527
3528
            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:
3529
                seq_lens = max_query_len
3530
            self.seq_lens.np[:num_reqs] = seq_lens
3531
3532
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3533

3534
3535
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3536
3537
            self.query_start_loc.copy_to_gpu()

3538
3539
3540
3541
3542
3543
3544
            attn_metadata, _ = self._build_attention_metadata(
                total_num_scheduled_tokens=num_tokens,
                max_num_scheduled_tokens=max_query_len,
                num_reqs=num_reqs,
                ubatch_slices=ubatch_slices,
                for_cudagraph_capture=True,
            )
3545

3546
        with self.maybe_dummy_run_with_lora(
3547
3548
3549
3550
3551
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
3552
        ):
3553
3554
3555
            # Make sure padding doesn't exceed max_num_tokens
            assert num_tokens_after_padding <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3556
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3557
                input_ids = None
3558
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3559
                model_kwargs = {
3560
                    **model_kwargs,
3561
3562
                    **self._dummy_mm_kwargs(num_reqs),
                }
3563
3564
            elif self.enable_prompt_embeds:
                input_ids = None
3565
3566
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3567
            else:
3568
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3569
                inputs_embeds = None
3570

3571
            if self.uses_mrope:
3572
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3573
            else:
3574
                positions = self.positions.gpu[:num_tokens_after_padding]
3575
3576
3577
3578
3579
3580
3581
3582
3583

            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,
3584
3585
3586
                            device=self.device,
                        )
                    )
3587
3588

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3589
                    num_tokens_after_padding, None, False
3590
                )
3591
3592

            # filter out the valid batch descriptor
3593
3594
3595
3596
3597
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3598
                        has_lora=activate_lora and self.lora_config is not None,
3599
3600
3601
3602
3603
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3604
3605
3606
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3607
3608
3609
3610
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3611
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3612
3613
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3614
3615
            else:
                cudagraph_runtime_mode = _cg_mode
3616

3617
            if ubatch_slices is not None:
3618
3619
3620
3621
3622
3623
3624
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
                num_tokens_after_padding = ubatch_slices[0].num_tokens
                if num_tokens_across_dp is not None:
                    num_tokens_across_dp[:] = num_tokens_after_padding

3625
3626
3627
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3628
3629
                    attn_metadata,
                    self.vllm_config,
3630
                    num_tokens=num_tokens_after_padding,
3631
3632
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3633
                    batch_descriptor=batch_descriptor,
3634
3635
3636
                    ubatch_slices=ubatch_slices,
                ),
            ):
3637
                outputs = self.model(
3638
3639
3640
3641
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3642
                    **model_kwargs,
3643
                )
3644

3645
3646
3647
3648
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3649

3650
            if self.speculative_config and self.speculative_config.use_eagle():
3651
                assert isinstance(self.drafter, EagleProposer)
3652
3653
3654
3655
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667

                # 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,
                )
3668

3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
        # 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)

3679
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3680
3681
3682
3683
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3684
3685
3686
3687
3688
3689

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3690
3691
3692
3693
        # 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)
3694

3695
        logits = self.model.compute_logits(hidden_states)
3696
3697
        num_reqs = logits.size(0)

3698
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713

        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)],
3714
            spec_token_ids=[[] for _ in range(num_reqs)],
3715
3716
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3717
            logitsprocs=LogitsProcessors(),
3718
        )
3719
        try:
3720
3721
3722
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3723
        except RuntimeError as e:
3724
            if "out of memory" in str(e):
3725
3726
3727
3728
                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 "
3729
3730
                    "initializing the engine."
                ) from e
3731
3732
            else:
                raise e
3733
        if self.speculative_config:
3734
3735
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3736
3737
                draft_token_ids, self.device
            )
3738
3739
3740
3741
3742
3743

            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
3744
3745
3746
3747
3748
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3749
            )
3750
3751
3752
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3753
                logits,
3754
3755
                dummy_metadata,
            )
3756
        return sampler_output
3757

3758
    def _dummy_pooler_run_task(
3759
3760
        self,
        hidden_states: torch.Tensor,
3761
3762
        task: PoolingTask,
    ) -> PoolerOutput:
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
        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

3774
        dummy_prompt_lens = torch.tensor(
3775
3776
            num_scheduled_tokens_list,
            device="cpu",
3777
        )
3778
3779
3780
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3781

3782
        model = cast(VllmModelForPooling, self.get_model())
3783
        dummy_pooling_params = PoolingParams(task=task)
3784
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3785
        to_update = model.pooler.get_pooling_updates(task)
3786
3787
        to_update.apply(dummy_pooling_params)

3788
        dummy_metadata = PoolingMetadata(
3789
3790
3791
3792
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3793

3794
3795
3796
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3797

3798
        try:
3799
3800
3801
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3802
        except RuntimeError as e:
3803
            if "out of memory" in str(e):
3804
                raise RuntimeError(
3805
3806
3807
                    "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 "
3808
3809
                    "initializing the engine."
                ) from e
3810
3811
            else:
                raise e
3812
3813
3814
3815
3816
3817
3818

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
            if self.scheduler_config.chunked_prefill_enabled:
                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."
                )

3839
        output_size = dict[PoolingTask, float]()
3840
        for task in supported_pooling_tasks:
3841
3842
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3843
            output_size[task] = sum(o.nbytes for o in output)
3844
3845
3846
3847
            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)
3848

3849
    def profile_run(self) -> None:
3850
        # Profile with multimodal encoder & encoder cache.
3851
        if self.supports_mm_inputs:
3852
            if self.model_config.multimodal_config.skip_mm_profiling:
3853
                logger.info(
3854
                    "Skipping memory profiling for multimodal encoder and "
3855
3856
                    "encoder cache."
                )
3857
3858
3859
3860
3861
3862
3863
3864
            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.
3865
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3866
3867
3868
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3869
3870
3871
3872
3873
3874
3875
3876
3877

                    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,
                    )
3878

3879
3880
3881
3882
3883
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3884

3885
                    # Run multimodal encoder.
3886
3887
3888
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3889

3890
3891
3892
3893
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3894

3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
3905
3906
                                (encoder_budget, encoder_output_shape[-1])
                            )
3907
3908
3909
3910
3911
3912
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3913
                    # Cache the dummy encoder outputs.
3914
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3915

3916
        # Add `is_profile` here to pre-allocate communication buffers
3917
3918
3919
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3920
        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)
3925
        else:
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            output = None
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        self._sync_device()
3928
        del hidden_states, output
3929
        self.encoder_cache.clear()
3930
        gc.collect()
3931

3932
    def capture_model(self) -> int:
3933
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3934
            logger.warning(
3935
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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3937
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3938
            return 0
3939

3940
3941
        compilation_counter.num_gpu_runner_capture_triggers += 1

3942
3943
        start_time = time.perf_counter()

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        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
3958
                    gc.collect()
3959

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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.
3963
        set_cudagraph_capturing_enabled(True)
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        with freeze_gc(), graph_capture(device=self.device):
3965
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3966
            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()
3979
                # 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,
                )
3988

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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
4001
                    if max_num_tokens >= x >= self.uniform_decode_query_len
4002
                ]
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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,
                )
4011

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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
4018
        # we may do lazy capturing in future that still allows capturing
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        # after here.
        set_cudagraph_capturing_enabled(False)
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        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
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        logger.info_once(
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            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4030
            scope="local",
4031
        )
4032
        return cuda_graph_size
4033

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    def _capture_cudagraphs(
        self,
4036
        compilation_cases: list[tuple[int, bool]],
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        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
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        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
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                    cudagraph_runtime_mode.name,
                ),
            )
4055

4056
        # We skip EPLB here since we don't want to record dummy metrics
4057
        for num_tokens, activate_lora in compilation_cases:
4058
            # 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,
                )
4071
            )
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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,
4088
                    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,
4097
                activate_lora=activate_lora,
4098
            )
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        self.maybe_remove_all_loras(self.lora_config)
4100

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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
4105
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
4106

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

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
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        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4114
            layers = get_layers_from_vllm_config(
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                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
4119
            # 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.
4124
            for layer_name in kv_cache_group_spec.layer_names:
4125
                attn_backend = layers[layer_name].get_attn_backend()
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                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

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                full_cls_name = attn_backend.full_cls_name()
                layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
4136
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4137
                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4141
                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()),
            )
4146
4147

        def create_attn_groups(
4148
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4149
            kv_cache_group_id: int,
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4151
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4152
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4153
                attn_group = AttentionGroup(
4154
                    attn_backend,
4155
                    layer_names,
4156
                    kv_cache_spec,
4157
                    kv_cache_group_id,
4158
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                )

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                attn_groups.append(attn_group)
            return attn_groups

4163
4164
        attention_backend_maps = []
        attention_backend_set: set[type[AttentionBackend]] = set()
4165
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4166
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4167
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            attention_backend_maps.append(attn_backends[0])
            attention_backend_set.update(attn_backends[1])

        # Resolve cudagraph_mode before actually initialize metadata_builders
        self._check_and_update_cudagraph_mode(attention_backend_set)

4173
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        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4175

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    def initialize_metadata_builders(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
        """
        Create the metadata builders for all KV cache groups and attn groups.
        """
        for kv_cache_group_id in range(len(kv_cache_config.kv_cache_groups)):
            for attn_group in self.attn_groups[kv_cache_group_id]:
                attn_group.create_metadata_builders(
                    self.vllm_config,
                    self.device,
                    kernel_block_sizes[kv_cache_group_id]
                    if kv_cache_group_id < len(kernel_block_sizes)
                    else None,
                    num_metadata_builders=1
                    if not self.parallel_config.enable_dbo
                    else 2,
                )
co63oc's avatar
co63oc committed
4194
        # Calculate reorder batch threshold (if needed)
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4196
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
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4198
        self.calculate_reorder_batch_threshold()

4199
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4201
    def _check_and_update_cudagraph_mode(
        self, attention_backends: set[type[AttentionBackend]]
    ) -> None:
4202
        """
4203
        Resolve the cudagraph_mode when there are multiple attention
4204
4205
4206
4207
        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4208
        min_cg_support = AttentionCGSupport.ALWAYS
4209
        min_cg_backend_name = None
4210

4211
4212
4213
4214
4215
        for attn_backend in attention_backends:
            builder_cls = attn_backend.get_builder_cls()
            if builder_cls.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder_cls.cudagraph_support
                min_cg_backend_name = attn_backend.__name__
4216
4217
4218
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
4219
4220
4221
4222
4223
4224
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4225
                f"with {min_cg_backend_name} backend (support: "
4226
4227
                f"{min_cg_support})"
            )
4228
4229
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4230
4231
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4232
                    "make sure compilation mode is VLLM_COMPILE"
4233
                )
4234
4235
4236
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4238
                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"
4239
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4240
                    CUDAGraphMode.FULL_AND_PIECEWISE
4241
                )
4242
4243
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4244
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4245
                    CUDAGraphMode.FULL_DECODE_ONLY
4246
                )
4247
4248
            logger.warning(msg)

4249
        # check that if we are doing decode full-cudagraphs it is supported
4250
4251
4252
4253
4254
4255
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4256
                f"with {min_cg_backend_name} backend (support: "
4257
4258
                f"{min_cg_support})"
            )
4259
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4260
4261
4262
4263
4264
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4265
                    "attention is compiled piecewise"
4266
4267
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4268
                    CUDAGraphMode.PIECEWISE
4269
                )
4270
            else:
4271
4272
                msg += (
                    "; setting cudagraph_mode=NONE because "
4273
                    "attention is not compiled piecewise"
4274
4275
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4276
                    CUDAGraphMode.NONE
4277
                )
4278
4279
            logger.warning(msg)

4280
4281
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4282
4283
4284
4285
4286
4287
4288
4289
        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 "
4290
                f"{min_cg_backend_name} (support: {min_cg_support})"
4291
            )
4292
4293
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4294
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4295
                    CUDAGraphMode.PIECEWISE
4296
                )
4297
4298
            else:
                msg += "; setting cudagraph_mode=NONE"
4299
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4300
                    CUDAGraphMode.NONE
4301
                )
4302
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4305
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4306
4307
4308
4309
4310
4311
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4312
                f"supported with {min_cg_backend_name} backend ("
4313
4314
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4315
                "and make sure compilation mode is VLLM_COMPILE"
4316
            )
4317

4318
4319
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4320
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4321
4322
            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4323

4324
4325
    def calculate_reorder_batch_threshold(self) -> None:
        """
4326
4327
4328
4329
        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.
4330
        """
4331
4332
4333
4334
4335
4336
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

        reorder_batch_thresholds = [
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
4337
4338
4339
4340
4341
        # 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
4342
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
4343

4344
4345
4346
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4347
4348
    ) -> int:
        """
4349
4350
4351
4352
4353
        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.
4354
4355
4356
4357
4358
4359

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

        Returns:
4360
            The selected block size
4361
4362

        Raises:
4363
            ValueError: If no valid block size found
4364
4365
        """

4366
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4373
        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
4374
                for supported_size in backend.supported_kernel_block_sizes:
4375
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4404
                    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
4405
            for supported_size in backend.supported_kernel_block_sizes
4406
4407
            if isinstance(supported_size, int)
        )
4408

4409
4410
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4414
        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}. ")
4415

4416
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4418
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
4419
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        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

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

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

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

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

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

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    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
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        if not self.kv_cache_config.kv_cache_groups:
            return
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        for attn_groups in self.attn_groups:
            yield from attn_groups
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    def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
        """
        Generate kernel_block_sizes that matches each block_size.

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

        Args:
            kv_cache_config: The KV cache configuration.

        Returns:
            list[int]: List of kernel block sizes for each cache group.
        """
        kernel_block_sizes = []
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        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
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            kv_cache_spec = kv_cache_group.kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                # All layers in the UniformTypeKVCacheSpecs have the same type,
                # Pick an arbitrary one to dispatch.
                kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
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                continue
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            elif isinstance(kv_cache_spec, AttentionSpec):
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                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
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                attn_groups = self.attn_groups[kv_cache_gid]
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                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
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                selected_kernel_size = self.select_common_block_size(
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                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
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            elif isinstance(kv_cache_spec, MambaSpec):
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                # This is likely Mamba or other non-attention cache,
                # no splitting.
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                kernel_block_sizes.append(kv_cache_spec.block_size)
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            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
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        kernel_block_sizes: list[int],
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    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            attn_backend = group.backend
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            if group.kv_cache_group_id == len(kernel_block_sizes):
                # There may be a last group for layers without kv cache.
                continue
            kernel_block_size = kernel_block_sizes[group.kv_cache_group_id]
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            for layer_name in group.layer_names:
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                if layer_name in self.runner_only_attn_layers:
                    continue
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                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
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                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
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                if isinstance(kv_cache_spec, AttentionSpec):
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                    has_attn = True
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                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
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                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

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                    kv_cache_shape = attn_backend.get_kv_cache_shape(
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                        kernel_num_blocks,
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                        kernel_block_size,
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                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
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                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
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                    dtype = kv_cache_spec.dtype
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                    try:
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                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
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                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
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                    except (AttributeError, NotImplementedError):
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                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
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4618
                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
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                else:
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                    raise NotImplementedError
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        if has_attn and has_mamba:
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            self._update_hybrid_attention_mamba_layout(kv_caches)
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        return kv_caches

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

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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            for layer_name in group.layer_names:
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                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
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                        f"a tensor of shape {kv_cache.shape}"
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                    )
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                    hidden_size = kv_cache.shape[2:].numel()
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                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
4676

4677
    def initialize_kv_cache_tensors(
4678
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
4679
    ) -> 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.

4687
        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
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        kv_caches = self._reshape_kv_cache_tensors(
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            kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
4696
        )
4697

4698
        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
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            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
4741

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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
        """
4749
        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
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        self.may_add_encoder_only_layers_to_kv_cache_config()
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        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
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        self.initialize_attn_backend(kv_cache_config)
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        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
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        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

4764
        # Reinitialize need to after initialize_attn_backend
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        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
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        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

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4776
        if has_kv_transfer_group():
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            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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4781
        if self.dcp_world_size > 1:
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            layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
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    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
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        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
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                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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4817
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
4818
        """
4819
        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.
        """

4826
        kv_cache_spec: dict[str, KVCacheSpec] = {}
4827
        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
4844

4845
        return kv_cache_spec
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4855

    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # 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.
4856
        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()
        return pinned.tolist()