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gpu_model_runner.py 202 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, 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_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.flash_attn import AttentionMetadata
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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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    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.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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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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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:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)  # type: ignore
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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
                )  # type: ignore
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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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        )
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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(
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                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
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        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
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        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
509

510
        self.reorder_batch_threshold: int | None = None
511

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        # Attention layers that are only in the KVCacheConfig of the runner
        # (e.g., KV sharing, encoder-only attention), but not in the
        # KVCacheConfig of the scheduler.
        self.runner_only_attn_layers: set[str] = set()

517
        # Cached outputs.
518
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
526

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

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

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

544
    def _make_buffer(
545
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
554

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557
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

558
        if not self.is_pooling_model:
559
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            return model_kwargs

561
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        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
563
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565

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

576
        seq_lens = self.seq_lens.gpu[:num_reqs]
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        token_type_ids = []

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

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

589
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
590
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        """
        Update the order of requests in the batch based on the attention
592
        backend's needs. For example, some attention backends (namely MLA) may
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        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

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

607
        if self.reorder_batch_threshold is not None:
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610
            # 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.
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            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
615
                assert self.reorder_batch_threshold == 1, (
616
                    "DCP not support reorder_batch_threshold > 1 now."
617
                )
618
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            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
621
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                decode_threshold=self.reorder_batch_threshold,
            )
623

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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
626
        """Initialize attributes from torch.cuda.get_device_properties"""
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        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

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

634
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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640
        """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.

641
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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
643
644
        """
        # Remove finished requests from the cached states.
645
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
654
            self.input_batch.remove_request(req_id)
655
656

        # Free the cached encoder outputs.
657
658
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
659

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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
673
            self.input_batch.remove_request(req_id)
674

675
        reqs_to_add: list[CachedRequestState] = []
676
        # Add new requests to the cached states.
677
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679
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
680
            pooling_params = new_req_data.pooling_params
681

682
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            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
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690
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

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            if self.is_pooling_model:
                assert pooling_params is not None
693
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                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
695

696
                model = cast(VllmModelForPooling, self.get_model())
697
                to_update = model.pooler.get_pooling_updates(task)
698
699
                to_update.apply(pooling_params)

700
            req_state = CachedRequestState(
701
                req_id=req_id,
702
                prompt_token_ids=new_req_data.prompt_token_ids,
703
                prompt_embeds=new_req_data.prompt_embeds,
704
                mm_features=new_req_data.mm_features,
705
                sampling_params=sampling_params,
706
                pooling_params=pooling_params,
707
                generator=generator,
708
709
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
710
                output_token_ids=[],
711
                lora_request=new_req_data.lora_request,
712
            )
713
714
            self.requests[req_id] = req_state

715
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
716
            if self.uses_mrope:
717
                self._init_mrope_positions(req_state)
718

719
            reqs_to_add.append(req_state)
720

721
        # Update the states of the running/resumed requests.
722
        is_last_rank = get_pp_group().is_last_rank
723
724
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
725
            req_state = self.requests[req_id]
726
727
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
728
            resumed_from_preemption = req_id in req_data.resumed_req_ids
729
            num_output_tokens = req_data.num_output_tokens[i]
730

731
            # Update the cached states.
732

733
            req_state.num_computed_tokens = num_computed_tokens
734
            req_index = self.input_batch.req_id_to_index.get(req_id)
735
736
737
738
739
740
741
742

            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.
743
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745
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
746
747
748
749
                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:
750
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
751
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755
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
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759
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
760
761
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
762

763
            # Update the block IDs.
764
            if not resumed_from_preemption:
765
766
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
767
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
768
                        block_ids.extend(new_ids)
769
            else:
770
                assert req_index is None
771
                assert new_block_ids is not None
772
773
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
774
                req_state.block_ids = new_block_ids
775
776
777
778
779

            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.
780
781
782
783
784
785
786

                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:]

787
                reqs_to_add.append(req_state)
788
789
790
                continue

            # Update the persistent batch.
791
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
792
            if new_block_ids is not None:
793
                self.input_batch.block_table.append_row(new_block_ids, req_index)
794
795
796
797
798
799
800

            # 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)
801
                self.input_batch.token_ids_cpu[
802
803
804
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
805
                self.input_batch.num_tokens[req_index] = end_token_index
806

807
            # Add spec_token_ids to token_ids_cpu.
808
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
809
                req_id, []
810
            )
811
812
813
814
815
            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[
816
817
                    req_index, start_index:end_token_index
                ] = spec_token_ids
818
819
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
820
821
822
823
824
825
826

            # 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
827

828
829
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
830
831
        for request in reqs_to_add:
            self.input_batch.add_request(request)
832

833
834
835
836
837
838
        # 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()
839

840
    def _update_states_after_model_execute(
841
842
        self, output_token_ids: torch.Tensor
    ) -> None:
843
844
845
846
847
848
849
850
851
852
853
854
        """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.
855
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857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
        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()
        )
875
876
877
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

878
879
880
881
882
883
    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
884
885
886
887
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
888
889
890
891
892
893
894
895
896
897
898
899
            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

900
901
902
903
904
905
906
907
908
909
910
        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,
911
            )
912
        )
913

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

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

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

938
        return mm_kwargs_combined
939

940
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
941
        if not self.is_multimodal_raw_input_only_model:
942
            return {}
943

944
945
946
947
948
        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)
949

950
951
952
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
953
        cumsum_dtype: np.dtype | None = None,
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
    ) -> 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

970
971
972
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
973
        """Prepare the input IDs for the current batch.
974

975
976
977
978
979
980
981
        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)
982
983
984
            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)
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
            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)
1003
                indices_match &= prev_index == flattened_index
1004
1005
1006
1007
1008
1009
                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)
1010
1011
1012
            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)
1013
1014
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1015
            # So input_ids.cpu will have all the input ids.
1016
1017
1018
1019
1020
1021
1022
            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_(
1023
1024
1025
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1026
1027
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1028
            return
1029
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1030
1031
1032
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1033
        prev_common_req_indices_tensor = torch.tensor(
1034
1035
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1036
1037
1038
1039
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1040
1041
1042
                prev_common_req_indices_tensor, 0
            ],
        )
1043

1044
1045
1046
1047
1048
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1049
    ) -> np.ndarray | None:
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
        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)
        for req_id in scheduler_output.scheduled_encoder_inputs:
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1062
    def _prepare_inputs(
1063
        self, scheduler_output: "SchedulerOutput"
1064
1065
1066
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1067
        SpecDecodeMetadata | None,
1068
        np.ndarray,
1069
        CommonAttentionMetadata | None,
1070
        int,
1071
1072
        UBatchSlices | None,
        torch.Tensor | None,
1073
1074
        bool,
    ]:
1075
1076
1077
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1078
1079
1080
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1081
1082
        ]
        """
1083
1084
1085
1086
1087
1088
1089
        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.
1090
        self.input_batch.block_table.commit_block_table(num_reqs)
1091
1092

        # Get the number of scheduled tokens for each request.
1093
1094
1095
1096
        req_ids = self.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
1097
1098
1099

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1100
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1101

1102
1103
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1104
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1105
1106

        # Get positions.
1107
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1108
1109
1110
1111
1112
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1113

1114
1115
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1116
        if self.uses_mrope:
1117
1118
            self._calc_mrope_positions(scheduler_output)

1119
1120
1121
1122
        # 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.
1123
1124
1125
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1126
        token_indices_tensor = torch.from_numpy(token_indices)
1127

1128
1129
1130
        # 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.
1131
1132
1133
1134
1135
1136
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1137
        if self.enable_prompt_embeds:
1138
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1139
1140
1141
1142
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1143
1144
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1145
1146
1147
1148
1149
1150
1151
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

        # 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:
1178
1179
1180
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1181
1182

                output_idx += num_sched
1183

1184
1185
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1186
1187

        # Prepare the attention metadata.
1188
        self.query_start_loc.np[0] = 0
1189
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1190
1191
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1192
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1193
        self.query_start_loc.copy_to_gpu()
1194
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1195

1196
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1197
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1198
1199
1200
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1201
1202
1203
1204
1205
1206
1207

        # 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

1208
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1209
1210
1211
1212
1213
1214
1215
            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,
1216
        )
1217

1218
        self.seq_lens.np[:num_reqs] = (
1219
1220
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1221
        # Fill unused with 0 for full cuda graph mode.
1222
1223
1224
1225
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
        seq_lens = self.seq_lens.gpu[:num_reqs]
        max_seq_len = self.seq_lens.np[:num_reqs].max().item()
1226

1227
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1228
1229
1230
1231
1232
1233
1234
        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)
1235
1236
1237
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1238
1239
1240

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1241
        # Copy the tensors to the GPU.
1242
1243
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1244
        if self.uses_mrope:
1245
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1246
1247
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1248
1249
                non_blocking=True,
            )
1250
1251
        else:
            # Common case (1D positions)
1252
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1253

1254
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1255
1256
1257
1258
1259
1260
1261
        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
1262
            num_draft_tokens = None
1263
1264
1265
1266
1267
1268
            spec_decode_metadata = None
        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)
1269
1270
1271
            # 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)
1272
1273
1274
1275
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1276
1277
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1278
1279
1280
1281
1282
1283
1284
1285
                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
                )
1286
            spec_decode_metadata = self._calc_spec_decode_metadata(
1287
1288
                num_draft_tokens, cu_num_tokens
            )
1289
            logits_indices = spec_decode_metadata.logits_indices
1290
1291

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1292
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1293
1294
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1295
1296
1297

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1298
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1299
1300
                logits_indices
            )
1301

1302
1303
1304
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1305
        use_cascade_attn = False
1306

1307
        # Used in the below loop.
1308
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1309
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1310
1311
1312
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1313
        spec_decode_common_attn_metadata = None
1314
1315
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1316
1317
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1318
1319
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1320

1321
1322
1323
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
1324
1325
            self.kv_cache_config.kv_cache_groups
        ):
1326
            encoder_seq_lens = self._get_encoder_seq_lens(
1327
1328
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1329

1330
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1331
1332
1333
1334
1335
                # 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,
1336
1337
1338
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1339
                    (total_num_scheduled_tokens,),
1340
1341
1342
                    dtype=torch.int64,
                    device=self.device,
                )
1343
1344
1345
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1346
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1347
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1348
1349
1350

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1351
1352
1353
1354
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = scheduler_output.num_common_prefix_blocks[
                    kv_cache_group_id
                ]
1355

1356
            common_attn_metadata = CommonAttentionMetadata(
1357
1358
1359
1360
1361
                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,
1362
1363
1364
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1365
                max_seq_len=max_seq_len,
1366
1367
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1368
1369
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1370
                causal=True,
1371
                encoder_seq_lens=encoder_seq_lens,
1372
1373
1374
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1375
1376
            )

1377
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1378
                if isinstance(self.drafter, EagleProposer):
1379
1380
1381
1382
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1383
1384
1385
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1386

1387
1388
1389
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1390
                builder = attn_group.get_metadata_builder()
1391
1392
1393
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1394
                        num_common_prefix_blocks,
1395
                        attn_group.kv_cache_spec,
1396
1397
                        builder,
                    )
1398

1399
                extra_attn_metadata_args = {}
1400
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1401
                    extra_attn_metadata_args = dict(
1402
1403
1404
1405
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1406
1407
                    )

1408
1409
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1410
1411
                        ubatch_slices, common_attn_metadata
                    )
1412
                    for ubid, common_attn_metadata in enumerate(
1413
1414
1415
1416
1417
1418
1419
1420
                        common_attn_metadata_list
                    ):
                        attn_metadata_i = attn_group.get_metadata_builder(
                            ubatch_id=ubid
                        ).build(
                            common_prefix_len=common_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                        )
1421
1422
1423
1424
1425
1426
1427
1428
                        for layer_name in kv_cache_group_spec.layer_names:
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
                    attn_metadata_i = builder.build(
                        common_prefix_len=common_prefix_len,
                        common_attn_metadata=common_attn_metadata,
1429
1430
1431
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1432
1433
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1434

1435
1436
1437
1438
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1439
1440
1441
1442
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1443
1444
1445
1446
1447
1448
1449
1450
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1451
            num_tokens_across_dp,
1452
1453
            use_cascade_attn,
        )
1454

1455
1456
1457
1458
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1459
1460
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1461
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    ) -> 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.
        """
1479
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1480
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        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]
1517
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1518
1519
1520
1521
1522
1523
1524
        # 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(
1525
1526
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1527
        # common_prefix_len should be a multiple of the block size.
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
        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
        )
1539
1540
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1541
1542
1543
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1544
            num_kv_heads=kv_cache_spec.num_kv_heads,
1545
            use_alibi=self.use_alibi,
1546
            use_sliding_window=use_sliding_window,
1547
            use_local_attention=use_local_attention,
1548
            num_sms=self.num_sms,
1549
            dcp_world_size=self.dcp_world_size,
1550
1551
1552
        )
        return common_prefix_len if use_cascade else 0

1553
1554
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1555
        for index, req_id in enumerate(self.input_batch.req_ids):
1556
1557
1558
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1559
1560
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1561
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1562
1563
                req.prompt_token_ids, req.prompt_embeds
            )
1564
1565

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1566
1567
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1568
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1570
1571
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1573
1574
1575
1576
1577
1578
1579
1580
            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

1581
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1583
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1584
1585
1586
1587
1588
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1590
                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

1591
                MRotaryEmbedding.get_next_input_positions_tensor(
1592
                    out=self.mrope_positions.np,
1593
1594
1595
1596
1597
                    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,
                )
1598
1599
1600

                mrope_pos_ptr += completion_part_len

1601
1602
    def _calc_spec_decode_metadata(
        self,
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
        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
1619
1620
1621
1622

        # 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(
1623
1624
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1625
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1626
        logits_indices = np.repeat(
1627
1628
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1629
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1630
1631
1632
1633
1634
1635
        logits_indices += arange

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

        # Compute the draft logits indices.
1636
1637
1638
        # 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(
1639
1640
            num_draft_tokens, cumsum_dtype=np.int32
        )
1641
1642
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1643
1644
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1645
1646
1647
1648
1649
        # [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(
1650
1651
            self.device, non_blocking=True
        )
1652
1653
1654
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1655
1656
1657
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1658
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1659
1660
            self.device, non_blocking=True
        )
1661
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1662
1663
            self.device, non_blocking=True
        )
1664

1665
1666
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1667
        draft_token_ids = self.input_ids.gpu[logits_indices]
1668
1669
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1670
        return SpecDecodeMetadata(
1671
1672
1673
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1674
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1675
1676
1677
1678
1679
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1680
1681
1682
1683
1684
1685
1686
    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
1687
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1688
1689
1690
1691
1692
        # 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_(
1693
1694
1695
1696
1697
1698
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1699
1700
1701
1702
1703
            # 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
1704
1705
1706
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1707
1708
        return logits_indices_padded

1709
1710
1711
1712
1713
1714
1715
1716
    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
1717
                inputs.
1718
1719
1720
1721
1722
1723

        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
        """
1724
1725
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1726
            return [], []
1727
        # Batch the multi-modal inputs.
1728
        mm_kwargs = list[MultiModalKwargsItem]()
1729
1730
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1731
1732
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1733
1734

            for mm_input_id in encoder_input_ids:
1735
1736
1737
1738
                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))
1739

1740
1741
1742
1743
1744
        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(
1745
1746
            scheduler_output
        )
1747
1748
1749
1750

        if not mm_kwargs:
            return

1751
1752
1753
1754
1755
1756
1757
        # 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.
1758
        model = cast(SupportsMultiModal, self.model)
1759
        encoder_outputs = []
1760
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1761
1762
1763
1764
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1765
        ):
1766
1767
1768
            curr_group_outputs = []

            # EVS-related change.
1769
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1770
            # processing multimodal data. This solves the issue with scheduler
1771
1772
1773
1774
            # 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)
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
            # 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,
                        )
1792
                    )
1793
1794

                    micro_batch_outputs = model.get_multimodal_embeddings(
1795
1796
                        **micro_batch_mm_inputs
                    )
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806

                    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.
1807
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1808

1809
1810
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1811
                expected_num_items=num_items,
1812
            )
1813
            encoder_outputs.extend(curr_group_outputs)
1814

1815
1816
1817
        # 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(
1818
1819
1820
1821
1822
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1823
1824
        self,
        scheduler_output: "SchedulerOutput",
1825
        shift_computed_tokens: int = 0,
1826
1827
1828
1829
1830
1831
1832
1833
    ) -> 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
1834
        should_sync_mrope_positions = False
1835

1836
        for req_id in self.input_batch.req_ids:
1837
1838
            mm_embeds_req: list[torch.Tensor] = []

1839
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1840
            req_state = self.requests[req_id]
1841
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1842

1843
1844
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1845
1846
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862

                # 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,
1863
1864
                    num_encoder_tokens,
                )
1865
                assert start_idx < end_idx
1866

1867
                mm_hash = mm_feature.identifier
1868
                encoder_output = self.encoder_cache.get(mm_hash, None)
1869
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1870
1871
1872
1873

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

1874
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1875
1876
1877
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1878

1879
1880
1881
1882
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1883
1884
1885
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1886
                assert req_state.mrope_positions is not None
1887
1888
1889
1890
1891
1892
1893
                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,
1894
1895
                    )
                )
1896
1897
1898
1899
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1900
1901
1902
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1903
1904
1905

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1906
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1907

1908
        return mm_embeds, is_mm_embed
1909

1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
    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
1926
        model = cast(SupportsMultiModal, self.model)
1927
1928
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1929
1930
1931
1932
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1933
1934
1935
1936
1937
1938
1939
1940
        ):
            # 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

1941
    def get_model(self) -> nn.Module:
1942
        # get raw model out of the cudagraph wrapper.
1943
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1944
            return self.model.unwrap()
1945
1946
        return self.model

1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
    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

1962
1963
1964
1965
1966
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1967
1968
        supported_tasks = list(model.pooler.get_supported_tasks())

1969
1970
1971
1972
1973
        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")
1974

1975
1976
            logger.debug_once(
                "Chunked prefill is not supported with "
1977
1978
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
1979
1980
1981
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1982
1983
1984
1985
1986

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1987
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1988
1989

        return supported_tasks
1990

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
    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)

2001
    def sync_and_slice_intermediate_tensors(
2002
2003
2004
2005
2006
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
2007
2008
2009
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2010
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2011
2012
2013
2014
2015
2016

        # 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():
2017
                is_scattered = k == "residual" and is_rs
2018
                copy_len = num_tokens // tp if is_scattered else num_tokens
2019
                self.intermediate_tensors[k][:copy_len].copy_(
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
                    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:
2033
2034
2035
2036
2037
2038
2039
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2040
2041
        model = self.get_model()
        assert is_mixture_of_experts(model)
2042
        self.eplb_state.step(
2043
            model,
2044
2045
            is_dummy,
            is_profile,
2046
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2047
2048
        )

2049
2050
2051
2052
    # 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)
2053
2054
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2055
2056
2057
2058
2059
2060
        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
        )
2061

2062
2063
2064
2065
2066
2067
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2068
2069
2070
        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"
        )
2071

2072
        hidden_states = hidden_states[:num_scheduled_tokens]
2073
        pooling_metadata = self.input_batch.get_pooling_metadata()
2074
2075
2076
2077
        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]
2078

2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
        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()
2089

2090
        pooler_output: list[torch.Tensor | None] = []
2091
        for raw_output, seq_len, prompt_len in zip(
2092
2093
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2094
            output = raw_output if seq_len == prompt_len else None
2095
            pooler_output.append(output)
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105

        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,
        )

2106
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2107
2108
2109
2110
2111
2112
        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]
        ):
2113
2114
2115
2116
2117
2118
2119
2120
            # 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
2121
2122
2123
2124
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2125
2126
2127
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2128
    def _preprocess(
2129
2130
        self,
        scheduler_output: "SchedulerOutput",
2131
        num_input_tokens: int,  # Padded
2132
        intermediate_tensors: IntermediateTensors | None = None,
2133
    ) -> tuple[
2134
2135
        torch.Tensor | None,
        torch.Tensor | None,
2136
        torch.Tensor,
2137
        IntermediateTensors | None,
2138
2139
        dict[str, Any],
    ]:
2140
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2141
        is_first_rank = get_pp_group().is_first_rank
2142

2143
2144
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2145
2146
        if (
            self.supports_mm_inputs
2147
            and is_first_rank
2148
2149
            and not self.model_config.is_encoder_decoder
        ):
2150
2151
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2152
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2153

2154
2155
2156
            # 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.
2157
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2158
2159
2160
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2161
            )
2162

2163
            # TODO(woosuk): Avoid the copy. Optimize.
2164
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2165

2166
            input_ids = None
2167
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2168
2169
2170
2171
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2172
        elif self.enable_prompt_embeds and is_first_rank:
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
            # 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).
2185
2186
2187
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2188
                .squeeze(1)
2189
            )
2190
2191
2192
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2193
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2194
2195
2196
2197
2198
                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
2199
        else:
2200
2201
2202
2203
            # 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.
2204
            input_ids = self.input_ids.gpu[:num_input_tokens]
2205
            inputs_embeds = None
2206
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2207
        if self.uses_mrope:
2208
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2209
        else:
2210
            positions = self.positions.gpu[:num_input_tokens]
2211

2212
        if is_first_rank:
2213
2214
            intermediate_tensors = None
        else:
2215
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2216
2217
                num_input_tokens, intermediate_tensors, True
            )
2218

2219
2220
2221
2222
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2223
2224
2225
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2226
2227
2228
2229
2230
2231
2232
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2233

2234
    def _sample(
2235
        self,
2236
2237
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2238
    ) -> SamplerOutput:
2239
        # Sample the next token and get logprobs if needed.
2240
        sampling_metadata = self.input_batch.sampling_metadata
2241
        if spec_decode_metadata is None:
2242
2243
2244
            # 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()
2245
            return self.sampler(
2246
2247
2248
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2249

2250
        sampler_output = self.rejection_sampler(
2251
2252
            spec_decode_metadata,
            None,  # draft_probs
2253
            logits,
2254
2255
            sampling_metadata,
        )
2256
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2257
2258
2259
        return sampler_output

    def _bookkeeping_sync(
2260
2261
2262
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2263
        logits: torch.Tensor | None,
2264
2265
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2266
        spec_decode_metadata: SpecDecodeMetadata | None,
2267
    ) -> tuple[
2268
        dict[str, int],
2269
        LogprobsLists | None,
2270
        list[list[int]],
2271
        dict[str, LogprobsTensors | None],
2272
2273
2274
        list[str],
        dict[str, int],
        list[int],
2275
    ]:
2276
2277
2278
2279
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2280
2281
2282
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2283
2284
2285
2286
        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)
2287

2288
2289
2290
        # 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()
2291
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2292
2293

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2294
        sampled_token_ids = sampler_output.sampled_token_ids
2295
        invalid_req_indices = []
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
        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:
2310
                valid_sampled_token_ids[int(i)].clear()
2311
        else:
2312
            valid_sampled_token_ids = []
2313
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2314
2315
2316
2317
2318
2319
            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.
2320
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2321
2322
2323
2324
2325
            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
            }
2326

2327
2328
2329
2330
2331
        # 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.
2332
        req_ids = self.input_batch.req_ids
2333
2334
2335
2336
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2337
2338
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2339
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2340
2341
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2342
2343
2344
2345
2346
2347
2348
2349

            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
                )

2350
2351
2352
2353
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2354
            end_idx = start_idx + num_sampled_ids
2355
2356
2357
2358
            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}"
2359
            )
2360

2361
2362
            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
2363
2364
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2365

2366
            req_id = req_ids[req_idx]
2367
2368
2369
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2370
2371
        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
2372
            if not self.use_async_scheduling and logprobs_tensors is not None
2373
2374
2375
2376
2377
2378
2379
2380
2381
            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,
        )

2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
        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,
        )

2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
    @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()

2407
2408
    def _model_forward(
        self,
2409
2410
2411
2412
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2413
2414
2415
2416
2417
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2418
        Motivation: We can inspect only this method versus
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
        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,
        )

2439
2440
2441
2442
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2443
        intermediate_tensors: IntermediateTensors | None = None,
2444
2445
2446
2447
2448
2449
2450
    ) -> 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
2451
        with record_function_or_nullcontext("Preprocess"):
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            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

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                if not num_scheduled_tokens:
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                    if not has_kv_transfer_group():
                        # Return empty ModelRunnerOutput if no work to do.
                        return EMPTY_MODEL_RUNNER_OUTPUT
                    return self.kv_connector_no_forward(
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                        scheduler_output, self.vllm_config
                    )
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                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
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                        "it when the requests need prompt logprobs"
                    )
2469

2470
                # Prepare the decoder inputs.
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                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
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                    num_tokens_across_dp,
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                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2482

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            dp_rank = self.parallel_config.data_parallel_rank
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            if ubatch_slices:
                assert num_tokens_across_dp is not None
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                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
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                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

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            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
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            ) = self._preprocess(
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                scheduler_output, num_input_tokens, intermediate_tensors
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            )

            uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
                num_scheduled_tokens == self.input_batch.num_reqs * max_query_len
            )
            batch_descriptor = BatchDescriptor(
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                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
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            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
2516

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        # Set cudagraph mode to none if calc_kv_scales is true.
        if attn_metadata is not None:
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            metadata_list = (
                attn_metadata.values()
                if isinstance(attn_metadata, dict)
                else [attn_metadata]
            )
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            if any(
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                getattr(m, "enable_kv_scales_calculation", False) for m in metadata_list
            ):
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                cudagraph_runtime_mode = CUDAGraphMode.NONE

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        # Run the model.
        # Use persistent buffers for CUDA graphs.
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        with (
            set_forward_context(
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                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                batch_descriptor=batch_descriptor,
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                ubatch_slices=ubatch_slices,
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            ),
            record_function_or_nullcontext("Forward"),
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2544
            model_output = self._model_forward(
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                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

        with record_function_or_nullcontext("Postprocess"):
            if self.use_aux_hidden_state_outputs:
2554
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
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                # Common case.
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                hidden_states = model_output
                aux_hidden_states = None

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

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                sample_hidden_states = hidden_states[logits_indices]
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                if not get_pp_group().is_last_rank:
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                    all_gather_tensors = {
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                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2589
                    }
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                    get_pp_group().send_tensor_dict(
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                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
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                        all_gather_tensors=all_gather_tensors,
                    )
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                    logits = None
                else:
2597
                    logits = self.model.compute_logits(sample_hidden_states)
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2600
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2602

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

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

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        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
            kv_connector_output,
        )
        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
            )
2648
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2650
2651

        with record_function_or_nullcontext("Sample"):
            sampler_output = self._sample(logits, spec_decode_metadata)

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        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
            with record_function_or_nullcontext("Draft"):
                self._draft_token_ids = self.propose_draft_token_ids(
                    scheduler_output,
                    sampled_token_ids,
                    self.input_batch.sampling_metadata,
                    hidden_states,
                    sample_hidden_states,
                    aux_hidden_states,
                    spec_decode_metadata,
                    spec_decode_common_attn_metadata,
                )

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        use_padded_batch_for_eagle = (
            self.speculative_config
            and self.speculative_config.use_eagle()
            and not self.speculative_config.disable_padded_drafter_batch
        )
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        effective_drafter_max_model_len = self.max_model_len
        if effective_drafter_max_model_len is None:
            effective_drafter_max_model_len = self.model_config.max_model_len
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        if (
            self.speculative_config
            and self.speculative_config.draft_model_config is not None
            and self.speculative_config.draft_model_config.max_model_len is not None
        ):
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            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
2682
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
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            spec_decode_common_attn_metadata.max_seq_len
            + self.speculative_config.num_speculative_tokens
            <= effective_drafter_max_model_len
        )
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        if use_padded_batch_for_eagle and input_fits_in_drafter:
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            # EAGLE speculative decoding can use the GPU sampled tokens
            # as inputs, and does not need to wait for bookkeeping to finish.
            propose_draft_token_ids(sampler_output.sampled_token_ids)

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        with record_function_or_nullcontext("Bookkeep"):
            (
                num_nans_in_logits,
                logprobs_lists,
                valid_sampled_token_ids,
                prompt_logprobs_dict,
                req_ids_output_copy,
                req_id_to_index_output_copy,
                invalid_req_indices,
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2703
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            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
2706
                scheduler_output.total_num_scheduled_tokens,
2707
                spec_decode_metadata,
2708
            )
2709

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        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
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            # ngram and other speculative decoding methods use the sampled
            # tokens on the CPU, so they are run after bookkeeping.
            propose_draft_token_ids(valid_sampled_token_ids)
2718

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2720
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2721

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2724
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
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2728
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2729
            kv_connector_output=kv_connector_output,
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2731
2732
            num_nans_in_logits=num_nans_in_logits,
        )

2733
2734
2735
        if not self.use_async_scheduling:
            return output

2736
        async_output = AsyncGPUModelRunnerOutput(
2737
            model_runner_output=output,
2738
            sampled_token_ids=sampler_output.sampled_token_ids,
2739
            logprobs_tensors=sampler_output.logprobs_tensors,
2740
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2742
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            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

2744
2745
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2750
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        # 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,
        )

        return async_output

2753
    def take_draft_token_ids(self) -> DraftTokenIds | None:
2754
2755
2756
2757
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2760
2761
2762
2763
        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)

2764
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2766
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2767
        sampled_token_ids: torch.Tensor | list[list[int]],
2768
2769
2770
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
2771
2772
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2773
        common_attn_metadata: CommonAttentionMetadata,
2774
    ) -> list[list[int]] | torch.Tensor:
2775
2776
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2777
            assert isinstance(sampled_token_ids, list)
2778
            assert isinstance(self.drafter, NgramProposer)
2779
            draft_token_ids = self.drafter.propose(
2780
2781
                sampled_token_ids,
                self.input_batch.req_ids,
2782
2783
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2784
2785
                self.input_batch.spec_decode_unsupported_reqs,
            )
2786
        elif self.speculative_config.method == "medusa":
2787
            assert isinstance(sampled_token_ids, list)
2788
            assert isinstance(self.drafter, MedusaProposer)
2789

2790
2791
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2792
2793
2794
2795
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
Wentao Ye's avatar
Wentao Ye committed
2796
                assert spec_decode_metadata is not None
2797
                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
2800
2801
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2802
                indices = torch.tensor(indices, device=self.device)
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2804
                hidden_states = sample_hidden_states[indices]

2805
            draft_token_ids = self.drafter.propose(
2806
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2808
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2809
        elif self.speculative_config.use_eagle():
2810
            assert isinstance(self.drafter, EagleProposer)
2811
2812
2813
2814
2815

            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.
2816
2817
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2818
                    "padded-batch is disabled."
2819
                )
2820
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2821
2822
2823
2824
2825
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2826
2827
2828
2829
2830
            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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2832
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2833
                    "padded-batch is enabled."
2834
2835
                )
                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,
2842
                        self.num_discarded_requests,
2843
                    )
2844
                )
Jiayi Yao's avatar
Jiayi Yao committed
2845

2846
            if spec_decode_metadata is None:
2847
                token_indices_to_sample = None
2848
                # input_ids can be None for multimodal models.
2849
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2850
                target_positions = self._get_positions(num_scheduled_tokens)
2851
                if self.use_aux_hidden_state_outputs:
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2852
                    assert aux_hidden_states is not None
2853
                    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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2857
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2858
            else:
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                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
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2862
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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,
                    )
2866
                else:
2867
                    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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2873
                            valid_sampled_tokens_count,
                        )
                    )
2874

2875
                target_token_ids = self.input_ids.gpu[token_indices]
2876
                target_positions = self._get_positions(token_indices)
2877
                if self.use_aux_hidden_state_outputs:
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2878
                    assert aux_hidden_states is not None
2879
                    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]
2884

2885
            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
2892

2893
            draft_token_ids = self.drafter.propose(
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2895
2896
2897
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2898
                last_token_indices=token_indices_to_sample,
2899
                sampling_metadata=sampling_metadata,
2900
                common_attn_metadata=common_attn_metadata,
2901
                mm_embed_inputs=mm_embed_inputs,
2902
            )
2903

2904
        return draft_token_ids
2905

2906
2907
2908
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2909
2910
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2911
                f"Allowed configs: {allowed_config_names}"
2912
            )
2913
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2915
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2917
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2921
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2922
2923
2924
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2926
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
2927
2928
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2929
2930
2931
2932
2933

            num_local_physical_experts = torch.empty(1, dtype=torch.int32, device="cpu")
            torch.distributed.broadcast(
                num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0
            )
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            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2936
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2937
            num_logical_experts = global_expert_load.shape[1]
2938
            self.parallel_config.eplb_config.num_redundant_experts = (
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2944
                num_local_physical_experts * new_ep_size - num_logical_experts
            )
            assert old_global_expert_indices.shape[1] % num_local_physical_experts == 0
            old_ep_size = (
                old_global_expert_indices.shape[1] // num_local_physical_experts
            )
2945
            rank_mapping = {
2946
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
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2948
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2951
2952
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2953
        with DeviceMemoryProfiler() as m:
2954
            time_before_load = time.perf_counter()
2955
            model_loader = get_model_loader(self.load_config)
2956
            self.model = model_loader.load_model(
2957
2958
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2959
            if self.lora_config:
2960
2961
2962
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2963
2964
2965
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2966
            if self.use_aux_hidden_state_outputs:
2967
                if not supports_eagle3(self.get_model()):
2968
2969
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2970
2971
                        "aux_hidden_state_outputs was requested"
                    )
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984

                # 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)
2985
            time_after_load = time.perf_counter()
2986
        self.model_memory_usage = m.consumed_memory
2987
        logger.info_once(
2988
2989
2990
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
2991
            scope="local",
2992
        )
2993
        prepare_communication_buffer_for_model(self.model)
2994

2995
        self.is_multimodal_pruning_enabled = (
2996
            supports_multimodal_pruning(self.get_model())
2997
2998
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2999

3000
3001
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
            logger.info("EPLB is enabled for model %s.", self.model_config.model)
3002
3003
3004
3005
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
3006
3007
3008
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3009
3010
            )

3011
        if (
3012
3013
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3014
            and supports_dynamo()
3015
        ):
3016
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3017
            compilation_counter.stock_torch_compile_count += 1
3018
            self.model.compile(fullgraph=True, backend=backend)
3019
            return
3020
        # for other compilation modes, cudagraph behavior is controlled by
3021
3022
3023
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3024
3025
3026
3027
3028
3029
3030
        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
            )
3031
3032
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3033
3034
3035
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3036
            else:
3037
3038
3039
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3040

3041
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
        """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

3065
    def reload_weights(self) -> None:
3066
        assert getattr(self, "model", None) is not None, (
3067
            "Cannot reload weights before model is loaded."
3068
        )
3069
3070
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3071
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3072

3073
3074
3075
3076
3077
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3078
            self.get_model(),
3079
            tensorizer_config=tensorizer_config,
3080
            model_config=self.model_config,
3081
3082
        )

3083
3084
3085
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3086
        num_scheduled_tokens: dict[str, int],
3087
    ) -> dict[str, LogprobsTensors | None]:
3088
3089
3090
3091
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3092
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3093
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3094
3095
3096
3097
3098

        # 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():
3099
            num_tokens = num_scheduled_tokens[req_id]
3100
3101
3102

            # Get metadata for this request.
            request = self.requests[req_id]
3103
3104
3105
3106
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3107
3108
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3109
3110
                self.device, non_blocking=True
            )
3111

3112
3113
3114
3115
3116
3117
            # 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(
3118
3119
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3120
3121
                in_progress_dict[req_id] = logprobs_tensors

3122
            # Determine number of logits to retrieve.
3123
3124
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3125
            num_remaining_tokens = num_prompt_tokens - start_tok
3126
            if num_tokens <= num_remaining_tokens:
3127
                # This is a chunk, more tokens remain.
3128
3129
3130
                # 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.
3131
3132
3133
3134
3135
                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)
3136
3137
3138
3139
3140
3141
3142
                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
3143
3144
3145
3146
3147

            # 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]
3148
            offset = self.query_start_loc.np[req_idx].item()
3149
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3150
            logits = self.model.compute_logits(prompt_hidden_states)
3151
3152
3153
3154

            # 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.
3155
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3156
3157

            # Compute prompt logprobs.
3158
3159
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3160
3161
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3162
3163

            # Transfer GPU->CPU async.
3164
3165
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3166
3167
3168
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3169
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3170
3171
                ranks, non_blocking=True
            )
3172
3173
3174
3175
3176

        # 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]
3177
            del in_progress_dict[req_id]
3178
3179

        # Must synchronize the non-blocking GPU->CPU transfers.
3180
        if prompt_logprobs_dict:
3181
            self._sync_device()
3182
3183
3184

        return prompt_logprobs_dict

3185
3186
    def _get_nans_in_logits(
        self,
3187
        logits: torch.Tensor | None,
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
    ) -> 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])
3199
3200
3201
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3202
3203
3204
3205
            return num_nans_in_logits
        except IndexError:
            return {}

3206
3207
3208
3209
3210
3211
    @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
3212
         - during DP rank dummy run
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
        """
        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(
3224
                    self.input_ids.gpu,
3225
3226
                    low=0,
                    high=self.model_config.get_vocab_size(),
3227
3228
                    dtype=input_ids.dtype,
                )
3229

3230
            logger.debug_once("Randomizing dummy data for DP Rank")
3231
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3232
3233
3234
            yield
            input_ids.fill_(0)

3235
3236
3237
3238
3239
3240
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3241
3242
        assert self.mm_budget is not None

3243
3244
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3245
            seq_len=self.max_model_len,
3246
            mm_counts={modality: 1},
3247
            cache=self.mm_budget.cache,
3248
3249
3250
3251
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3252
3253
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3254

3255
        model = cast(SupportsMultiModal, self.model)
3256
3257
3258
3259
3260
3261
3262
3263
3264
        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,
            )
        )
3265

3266
3267
3268
3269
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3270
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3271
3272
        force_attention: bool = False,
        uniform_decode: bool = False,
3273
        allow_microbatching: bool = True,
3274
3275
        skip_eplb: bool = False,
        is_profile: bool = False,
3276
        create_mixed_batch: bool = False,
3277
        remove_lora: bool = True,
3278
        activate_lora: bool = False,
3279
    ) -> tuple[torch.Tensor, torch.Tensor]:
3280
3281
3282
3283
3284
3285
3286
        """
        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.
3287
                - if not set will determine the cudagraph mode based on using
3288
                    the self.cudagraph_dispatcher.
3289
3290
3291
3292
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3293
            force_attention: If True, always create attention metadata. Used to
3294
3295
3296
3297
                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.
3298
3299
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3300
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3301
            activate_lora: If False, dummy_run is performed without LoRAs.
3302
        """
3303
3304
3305
3306
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3307

3308
        # If cudagraph_mode.decode_mode() == FULL and
3309
        # cudagraph_mode.separate_routine(). This means that we are using
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
        # 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.
3321
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3322

3323
3324
3325
3326
3327
        # 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
3328
3329
3330
3331
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3332
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3333
3334
3335
3336
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3337
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3338
3339
3340
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3341
            assert not create_mixed_batch
3342
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3343
3344
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3345
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3346
3347
3348
3349
3350
3351
        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

3352
3353
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3354
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3355
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3356

3357
3358
3359
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3360
3361
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3362
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3363
3364
3365
3366
3367
3368
3369
            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,
3370
3371
3372
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3373
3374
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3375

3376
        attn_metadata: PerLayerAttnMetadata | None = None
3377
3378
3379

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3380
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3381
            attn_metadata = {}
3382
3383
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3384

3385
3386
3387
3388
3389
3390
            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:
3391
                seq_lens = max_query_len
3392
            self.seq_lens.np[:num_reqs] = seq_lens
3393
3394
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3395

3396
3397
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3398
3399
            self.query_start_loc.copy_to_gpu()

3400
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3401
3402
                self.kv_cache_config.kv_cache_groups
            ):
3403
                common_attn_metadata = CommonAttentionMetadata(
3404
3405
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3406
3407
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3408
3409
3410
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3411
3412
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3413
                    max_query_len=max_query_len,
3414
                    max_seq_len=self.max_model_len,
3415
3416
3417
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3418
                    slot_mapping=self.input_batch.block_table[
3419
3420
3421
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3422
3423
3424
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3425
                )
3426
                for attn_group in self.attn_groups[kv_cache_group_id]:
3427
3428
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3429
3430
                            ubatch_slices, common_attn_metadata
                        )
3431
                        for ubid, common_attn_metadata in enumerate(
3432
3433
                            common_attn_metadata_list
                        ):
3434
                            assert common_attn_metadata.max_query_len == 1
3435
3436
3437
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3438
                            for layer_name in attn_group.layer_names:
3439
                                assert type(attn_metadata) is list
3440
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3441
3442
                    else:
                        assert type(attn_metadata) is dict
3443
3444
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3445
3446
                            common_attn_metadata
                        )
3447
                        for layer_name in attn_group.layer_names:
3448
                            attn_metadata[layer_name] = attn_metadata_i
3449

3450
        with self.maybe_dummy_run_with_lora(
3451
            self.lora_config, num_scheduled_tokens, activate_lora, remove_lora
3452
        ):
3453
3454
3455
            # 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)
3456
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3457
                input_ids = None
3458
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3459
                model_kwargs = {
3460
                    **model_kwargs,
3461
3462
                    **self._dummy_mm_kwargs(num_reqs),
                }
3463
3464
            elif self.enable_prompt_embeds:
                input_ids = None
3465
3466
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3467
            else:
3468
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3469
                inputs_embeds = None
3470

3471
            if self.uses_mrope:
3472
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3473
            else:
3474
                positions = self.positions.gpu[:num_tokens_after_padding]
3475
3476
3477
3478
3479
3480
3481
3482
3483

            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,
3484
3485
3486
                            device=self.device,
                        )
                    )
3487
3488

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3489
                    num_tokens_after_padding, None, False
3490
                )
3491
3492

            # filter out the valid batch descriptor
3493
3494
3495
3496
3497
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3498
                        has_lora=activate_lora and self.lora_config is not None,
3499
3500
3501
3502
3503
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3504
3505
3506
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3507
3508
3509
3510
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3511
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3512
3513
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3514
3515
            else:
                cudagraph_runtime_mode = _cg_mode
3516

3517
            if ubatch_slices is not None:
3518
3519
3520
3521
3522
3523
3524
                # 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

3525
3526
3527
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3528
3529
                    attn_metadata,
                    self.vllm_config,
3530
                    num_tokens=num_tokens_after_padding,
3531
3532
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3533
                    batch_descriptor=batch_descriptor,
3534
3535
3536
                    ubatch_slices=ubatch_slices,
                ),
            ):
3537
                outputs = self.model(
3538
3539
3540
3541
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3542
                    **model_kwargs,
3543
                )
3544

3545
3546
3547
3548
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3549

3550
            if self.speculative_config and self.speculative_config.use_eagle():
3551
                assert isinstance(self.drafter, EagleProposer)
3552
3553
3554
3555
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3556
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3557

3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
        # 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)

3568
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3569
3570
3571
3572
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3573
3574
3575
3576
3577
3578

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3579
3580
3581
3582
        # 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)
3583

3584
        logits = self.model.compute_logits(hidden_states)
3585
3586
        num_reqs = logits.size(0)

3587
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602

        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)],
3603
            spec_token_ids=[[] for _ in range(num_reqs)],
3604
3605
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3606
            logitsprocs=LogitsProcessors(),
3607
        )
3608
        try:
3609
3610
3611
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3612
        except RuntimeError as e:
3613
            if "out of memory" in str(e):
3614
3615
3616
3617
                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 "
3618
3619
                    "initializing the engine."
                ) from e
3620
3621
            else:
                raise e
3622
        if self.speculative_config:
3623
3624
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3625
3626
                draft_token_ids, self.device
            )
3627
3628
3629
3630
3631
3632

            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
3633
3634
3635
3636
3637
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3638
            )
3639
3640
3641
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3642
                logits,
3643
3644
                dummy_metadata,
            )
3645
        return sampler_output
3646

3647
    def _dummy_pooler_run_task(
3648
3649
        self,
        hidden_states: torch.Tensor,
3650
3651
        task: PoolingTask,
    ) -> PoolerOutput:
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
        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

3663
        dummy_prompt_lens = torch.tensor(
3664
3665
            num_scheduled_tokens_list,
            device="cpu",
3666
        )
3667
3668
3669
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3670

3671
        model = cast(VllmModelForPooling, self.get_model())
3672
        dummy_pooling_params = PoolingParams(task=task)
3673
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3674
        to_update = model.pooler.get_pooling_updates(task)
3675
3676
        to_update.apply(dummy_pooling_params)

3677
        dummy_metadata = PoolingMetadata(
3678
3679
3680
3681
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3682

3683
3684
3685
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3686

3687
        try:
3688
3689
3690
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3691
        except RuntimeError as e:
3692
            if "out of memory" in str(e):
3693
                raise RuntimeError(
3694
3695
3696
                    "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 "
3697
3698
                    "initializing the engine."
                ) from e
3699
3700
            else:
                raise e
3701
3702
3703
3704
3705
3706
3707

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
        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."
                )

3728
        output_size = dict[PoolingTask, float]()
3729
        for task in supported_pooling_tasks:
3730
3731
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3732
            output_size[task] = sum(o.nbytes for o in output)
3733
3734
3735
3736
            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)
3737

3738
    def profile_run(self) -> None:
3739
        # Profile with multimodal encoder & encoder cache.
3740
        if self.supports_mm_inputs:
3741
            if self.model_config.multimodal_config.skip_mm_profiling:
3742
                logger.info(
3743
                    "Skipping memory profiling for multimodal encoder and "
3744
3745
                    "encoder cache."
                )
3746
3747
3748
3749
3750
3751
3752
3753
            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.
3754
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3755
3756
3757
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3758
3759
3760
3761
3762
3763
3764
3765
3766

                    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,
                    )
3767

3768
3769
3770
3771
3772
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3773

3774
                    # Run multimodal encoder.
3775
3776
3777
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3778

3779
3780
3781
3782
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3783

3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
                    # 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(
3794
3795
                                (encoder_budget, encoder_output_shape[-1])
                            )
3796
3797
3798
3799
3800
3801
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3802
                    # Cache the dummy encoder outputs.
3803
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3804

3805
        # Add `is_profile` here to pre-allocate communication buffers
3806
3807
3808
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3809
        if get_pp_group().is_last_rank:
3810
3811
3812
3813
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3814
        else:
3815
            output = None
3816
        self._sync_device()
3817
        del hidden_states, output
3818
        self.encoder_cache.clear()
3819
        gc.collect()
3820

3821
    def capture_model(self) -> int:
3822
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3823
            logger.warning(
3824
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3825
3826
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3827
            return 0
3828

3829
3830
        compilation_counter.num_gpu_runner_capture_triggers += 1

3831
3832
        start_time = time.perf_counter()

3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
        @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()
3847
                    gc.collect()
3848

3849
3850
3851
        # 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.
3852
        set_cudagraph_capturing_enabled(True)
3853
        with freeze_gc(), graph_capture(device=self.device):
3854
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3855
            cudagraph_mode = self.compilation_config.cudagraph_mode
3856
            assert cudagraph_mode is not None
3857
3858
3859
3860
3861
3862
3863
3864
3865

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

3866
3867
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
3868
                # make sure we capture the largest batch size first
3869
3870
3871
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
3872
3873
3874
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3875
3876
                    uniform_decode=False,
                )
3877

3878
3879
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3880
3881
3882
3883
3884
3885
3886
            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
                )
3887
                decode_cudagraph_batch_sizes = [
3888
3889
                    x
                    for x in self.cudagraph_batch_sizes
3890
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3891
                ]
3892
3893
3894
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
3895
3896
3897
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3898
3899
                    uniform_decode=True,
                )
3900

3901
3902
3903
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3904
3905
3906
        # 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
3907
        # we may do lazy capturing in future that still allows capturing
3908
3909
        # after here.
        set_cudagraph_capturing_enabled(False)
3910
3911
3912
3913
3914

        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.
3915
        logger.info_once(
3916
3917
3918
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
3919
            scope="local",
3920
        )
3921
        return cuda_graph_size
3922

3923
3924
    def _capture_cudagraphs(
        self,
3925
        compilation_cases: list[tuple[int, bool]],
3926
3927
3928
3929
3930
3931
3932
        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}"
3933
3934
3935
3936
3937
3938
3939
3940

        # 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,
                ),
            )
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        # We skip EPLB here since we don't want to record dummy metrics
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        for num_tokens, activate_lora in compilation_cases:
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            # We currently only capture ubatched graphs when its a FULL
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            # cudagraph, a uniform decode batch, and the number of tokens
            # is above the threshold. Otherwise we just capture a non-ubatched
            # version of the graph
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            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
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                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
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            )
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            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
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                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
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                    activate_lora=activate_lora,
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                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
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                activate_lora=activate_lora,
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            )
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        self.maybe_remove_all_loras(self.lora_config)
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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
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        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
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        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

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

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

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                attn_groups.append(attn_group)
            return attn_groups

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        attention_backend_maps = []
        attention_backend_set: set[type[AttentionBackend]] = set()
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        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
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            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
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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)

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

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    def _check_and_update_cudagraph_mode(
        self, attention_backends: set[type[AttentionBackend]]
    ) -> None:
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        """
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        Resolve the cudagraph_mode when there are multiple attention
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        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
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        min_cg_backend_name = None
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        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__
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        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
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        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
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                f"with {min_cg_backend_name} backend (support: "
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                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4121
                    "make sure compilation mode is VLLM_COMPILE"
4122
                )
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                raise ValueError(msg)

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

4138
        # check that if we are doing decode full-cudagraphs it is supported
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        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
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                f"with {min_cg_backend_name} backend (support: "
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                f"{min_cg_support})"
            )
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            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
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                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
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                    "attention is compiled piecewise"
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                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.PIECEWISE
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                )
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            else:
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                msg += (
                    "; setting cudagraph_mode=NONE because "
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                    "attention is not compiled piecewise"
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                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.NONE
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                )
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            logger.warning(msg)

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

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
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        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4201
                f"supported with {min_cg_backend_name} backend ("
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                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4204
                "and make sure compilation mode is VLLM_COMPILE"
4205
            )
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        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4209
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4212

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    def calculate_reorder_batch_threshold(self) -> None:
        """
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4218
        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.
4219
        """
4220
4221
4222
4223
4224
4225
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

        reorder_batch_thresholds = [
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
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4230
        # 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
4231
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
4232

4233
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    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
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4237
    ) -> int:
        """
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        Select a block size that is supported by all backends and is a factor of
        kv_manager_block_size.

        If kv_manager_block_size is supported by all backends, return it directly.
        Otherwise, return the max supported size.
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4245
4246
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4248

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

        Returns:
4249
            The selected block size
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4251

        Raises:
4252
            ValueError: If no valid block size found
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4254
        """

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        def block_size_is_supported(
            backends: list[type[AttentionBackend]], block_size: int
        ) -> bool:
            """
            Check if the block size is supported by all backends.
            """
            for backend in backends:
                is_supported = False
                for supported_size in backend.get_supported_kernel_block_size():
                    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
            for supported_size in backend.get_supported_kernel_block_size()
            if isinstance(supported_size, int)
        )
4297

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        for supported_size in sorted(all_int_supported_sizes, reverse=True):
            if kv_manager_block_size % supported_size != 0:
                continue
            if block_size_is_supported(backends, supported_size):
                return supported_size
        raise ValueError(f"No common block size for {kv_manager_block_size}. ")
4304

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

        Args:
            kv_cache_config: The KV cache configuration.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
4316
4317
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4319
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
4320
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4321
        ]
4322
4323
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4325

        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,
4342
                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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4348
                    if self.vllm_config.speculative_config
                    else 0
                ),
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            )

4351
    def _allocate_kv_cache_tensors(
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4353
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4354
        """
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4357
        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.

4358
        Args:
4359
            kv_cache_config: The KV cache config
4360
        Returns:
4361
            dict[str, torch.Tensor]: A map between layer names to their
4362
            corresponding memory buffer for KV cache.
4363
        """
4364
4365
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4366
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4368
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4369
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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:
4374
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4376
4377
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4378
4379
4380
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4381
4382
        return kv_cache_raw_tensors

4383
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4385
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4386
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4387
4388
        if not self.kv_cache_config.kv_cache_groups:
            return
4389
4390
        for attn_groups in self.attn_groups:
            yield from attn_groups
4391

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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 = []
        for kv_cache_group_id, kv_cache_group in enumerate(
            kv_cache_config.kv_cache_groups
        ):
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4414
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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):
4416
                continue
4417
            elif isinstance(kv_cache_spec, AttentionSpec):
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4422
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
                attn_groups = self.attn_groups[kv_cache_group_id]
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
4423
                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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4509
                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:]),
                    )
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    def initialize_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
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    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

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

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        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # 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
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        )
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        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
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            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
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    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
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        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
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        self.may_add_encoder_only_layers_to_kv_cache_config()
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        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
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        self.initialize_attn_backend(kv_cache_config)
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        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
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        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

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

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4667
        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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4671

4672
        if self.dcp_world_size > 1:
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            layer_names = self.attn_groups[0][0].layer_names
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            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase, layer_names
            )
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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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4705
            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)
            )
4710

4711
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
4712
        """
4713
        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.
        """

4720
        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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4722
        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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4739
        return kv_cache_spec
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4749

    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.
4750
        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()