gpu_model_runner.py 231 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import itertools
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import time
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from collections import defaultdict
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from collections.abc import Iterator, Sequence
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from contextlib import contextmanager
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from copy import copy, deepcopy
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from functools import reduce
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from itertools import product
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionMetadata,
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    AttentionType,
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    MultipleOf,
)
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from vllm.attention.layer import Attention, MLAAttention
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
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from vllm.config import (
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    CompilationMode,
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    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
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from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
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from vllm.distributed.parallel_state import (
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    get_dcp_group,
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    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
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from vllm.forward_context import BatchDescriptor, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (
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    SupportsMRoPE,
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    SupportsMultiModal,
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    SupportsXDRoPE,
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    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
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    supports_xdrope,
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)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    get_dcp_local_seq_lens,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
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    ECConnectorOutput,
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    KVConnectorOutput,
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    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
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    make_empty_encoder_model_runner_output,
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)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.logits_processor.interface import LogitsProcessor
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
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if TYPE_CHECKING:
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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logger = init_logger(__name__)

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

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


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

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

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

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

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

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

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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
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        self.prepare_inputs_event: torch.Event | None = None
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        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
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            self.prepare_inputs_event = torch.Event()
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        # self.cudagraph_batch_sizes sorts in ascending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
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            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
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            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
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        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
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        )
        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            # Similar to mrope but use assigned dimension number for RoPE, 4 as default.
            self.xdrope_positions = self._make_buffer(
                (self.uses_xdrope_dim, self.max_num_tokens + 1), dtype=torch.int64
            )

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        # None in the first PP rank. The rest are set after load_model.
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        self.intermediate_tensors: IntermediateTensors | None = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

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

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

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

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

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

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

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

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

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

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

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

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

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        if not self.is_pooling_model:
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            return model_kwargs

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

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

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

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

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

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

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

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

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

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

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

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

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

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

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            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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            if self.uses_mrope:
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                self._init_mrope_positions(req_state)
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            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

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

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

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                reqs_to_add.append(req_state)
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                continue

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

            # 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.
985
986
            self.input_batch.spec_token_ids[req_index].clear()
            self.input_batch.spec_token_ids[req_index].extend(spec_token_ids)
987

988
989
990
991
992
993
994
995
996
            # there are no draft tokens with async scheduling,
            # we clear the spec_decoding info in scheduler_output and
            # use normal sampling but rejection_sampling.
            if self.use_async_scheduling:
                req_state.prev_num_draft_len = num_spec_tokens
                if num_spec_tokens and self._draft_token_ids is None:
                    scheduler_output.total_num_scheduled_tokens -= num_spec_tokens
                    scheduler_output.num_scheduled_tokens[req_id] -= num_spec_tokens
                    scheduler_output.scheduled_spec_decode_tokens.pop(req_id, None)
997
998
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
999
1000
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1001

1002
1003
1004
1005
1006
1007
        # 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()
1008

1009
    def _update_states_after_model_execute(
1010
1011
        self, output_token_ids: torch.Tensor
    ) -> None:
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
        """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.
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
        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()
        )
1044
1045
1046
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

1047
    def _init_mrope_positions(self, req_state: CachedRequestState):
1048
1049
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1050
1051
1052
1053
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1054
1055

        req_state.mrope_positions, req_state.mrope_position_delta = (
1056
            mrope_model.get_mrope_input_positions(
1057
                req_state.prompt_token_ids,
1058
                req_state.mm_features,
1059
            )
1060
        )
1061

1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
    def _init_xdrope_positions(self, req_state: CachedRequestState):
        model = self.get_model()
        xdrope_model = cast(SupportsXDRoPE, model)
        assert req_state.prompt_token_ids is not None, (
            "XD-RoPE requires prompt_token_ids to be available."
        )
        assert supports_xdrope(model), "XD-RoPE support is not implemented."

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

1075
    def _extract_mm_kwargs(
1076
        self,
1077
1078
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1079
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1080
            return {}
1081

1082
1083
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
1084
1085
1086
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
1087

1088
        # Input all modalities at once
1089
        model = cast(SupportsMultiModal, self.model)
1090
1091
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1092
1093
1094
1095
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1096
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1097
1098
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1099

1100
        return mm_kwargs_combined
1101

1102
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1103
        if not self.is_multimodal_raw_input_only_model:
1104
            return {}
1105

1106
1107
1108
1109
1110
        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)
1111

1112
1113
1114
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1115
        cumsum_dtype: np.dtype | None = None,
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
    ) -> 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

1132
    def _prepare_input_ids(
1133
1134
1135
1136
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1137
    ) -> None:
1138
        """Prepare the input IDs for the current batch.
1139

1140
1141
1142
1143
1144
1145
1146
        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)
1147
1148
1149
            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)
1150
1151
1152
1153
1154
1155
1156
            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
1157
1158
1159
1160
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1161
1162
        indices_match = True
        max_flattened_index = -1
1163
1164
1165
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1166
1167
1168
1169
1170
        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.
1171
1172
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1173
                flattened_index = cu_num_tokens[cur_index].item() - 1
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
                # example: cu_num_tokens = [2, 5, 8], draft_tokens = [1, 2, 2]
                # sample_flattened_indices = [0, 2, 5]
                # spec_flattened_indices = [1,   3, 4,    6, 7]
                sample_flattened_indices.append(flattened_index - draft_len)
                spec_flattened_indices.extend(
                    range(flattened_index - draft_len + 1, flattened_index + 1)
                )
                start = prev_index * self.num_spec_tokens
                # prev_draft_token_indices is used to find which draft_tokens_id
                # should be copied to input_ids
                # example: prev draft_tokens_id [[1,2], [3,4], [5, 6]]
                # flatten draft_tokens_id [1,2,3,4,5,6]
                # draft_len of each request [1, 2, 1]
                # then prev_draft_token_indices is [0,   2, 3,   4]
                prev_draft_token_indices.extend(range(start, start + draft_len))
1189
                indices_match &= prev_index == flattened_index
1190
                max_flattened_index = max(max_flattened_index, flattened_index)
1191
1192
1193
        num_commmon_tokens = len(sample_flattened_indices)
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
        if num_commmon_tokens < total_without_spec:
1194
1195
1196
            # 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)
1197
1198
1199
            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)
1200
1201
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1202
            # So input_ids.cpu will have all the input ids.
1203
1204
1205
1206
1207
1208
1209
            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_(
1210
1211
1212
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1213
1214
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1215
            return
1216
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1217
1218
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1219
        ).to(self.device, non_blocking=True)
1220
        prev_common_req_indices_tensor = torch.tensor(
1221
1222
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1223
1224
        self.input_ids.gpu.scatter_(
            dim=0,
1225
            index=sampled_tokens_index_tensor,
1226
            src=self.input_batch.prev_sampled_token_ids[
1227
1228
1229
                prev_common_req_indices_tensor, 0
            ],
        )
1230

1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
        # Scatter the draft tokens after the sampled tokens are scattered.
        if self._draft_token_ids is None or not spec_flattened_indices:
            return

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

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

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

1254
1255
    def _get_encoder_seq_lens(
        self,
1256
        num_scheduled_tokens: dict[str, int],
1257
1258
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1259
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1260
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1261
            return None, None
1262
1263
1264

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1265
        for req_id in num_scheduled_tokens:
1266
            req_index = self.input_batch.req_id_to_index[req_id]
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
            req_state = self.requests[req_id]
            if req_state.mm_features is None:
                self.encoder_seq_lens.np[req_index] = 0
                continue

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

        self.encoder_seq_lens.copy_to_gpu(num_reqs)
        encoder_seq_lens = self.encoder_seq_lens.gpu[:num_reqs]
        encoder_seq_lens_cpu = self.encoder_seq_lens.np[:num_reqs]
1283

1284
        return encoder_seq_lens, encoder_seq_lens_cpu
1285

1286
    def _prepare_inputs(
1287
1288
1289
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1290
1291
    ) -> tuple[
        torch.Tensor,
1292
        SpecDecodeMetadata | None,
1293
    ]:
1294
1295
        """
        :return: tuple[
1296
            logits_indices, spec_decode_metadata,
1297
1298
        ]
        """
1299
1300
1301
1302
1303
1304
1305
        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.
1306
        self.input_batch.block_table.commit_block_table(num_reqs)
1307
1308
1309

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1310
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1311

1312
1313
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1314
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1315
1316

        # Get positions.
1317
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1318
1319
1320
1321
1322
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1323

1324
1325
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1326
        if self.uses_mrope:
1327
1328
            self._calc_mrope_positions(scheduler_output)

1329
1330
1331
1332
1333
        # Calculate XD-RoPE positions.
        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            self._calc_xdrope_positions(scheduler_output)

1334
1335
1336
1337
        # 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.
1338
1339
1340
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1341
        token_indices_tensor = torch.from_numpy(token_indices)
1342

1343
1344
1345
        # 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.
1346
1347
1348
1349
1350
1351
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1352
        if self.enable_prompt_embeds:
1353
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1354
1355
1356
1357
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1358
1359
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392

        # 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:
1393
1394
1395
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1396
1397

                output_idx += num_sched
1398

1399
1400
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1401
1402

        # Prepare the attention metadata.
1403
        self.query_start_loc.np[0] = 0
1404
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1405
1406
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1407
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1408
        self.query_start_loc.copy_to_gpu()
1409
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1410

1411
        self.seq_lens.np[:num_reqs] = (
1412
1413
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1414
        # Fill unused with 0 for full cuda graph mode.
1415
1416
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1417

1418
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1419
1420
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1421
        # Record which requests should not be sampled,
1422
        # so that we could clear the sampled tokens before returning
1423
1424
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1425
        )
1426
        self.discard_request_mask.copy_to_gpu(num_reqs)
1427

1428
        # Copy the tensors to the GPU.
1429
1430
1431
1432
1433
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1434

1435
        if self.uses_mrope:
1436
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1437
1438
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1439
1440
                non_blocking=True,
            )
1441
1442
1443
1444
1445
1446
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
                non_blocking=True,
            )
1447
1448
        else:
            # Common case (1D positions)
1449
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1450

1451
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1452
1453
1454
1455
1456
1457
1458
        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
1459
            num_draft_tokens = None
1460
            spec_decode_metadata = None
1461
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1462
1463
1464
1465
1466
        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)
1467
1468
1469
            # 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)
1470
1471
1472
1473
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1474
1475
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1476
1477
1478
1479
1480
1481
1482
1483
                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
                )
1484
            spec_decode_metadata = self._calc_spec_decode_metadata(
1485
1486
                num_draft_tokens, cu_num_tokens
            )
1487
            logits_indices = spec_decode_metadata.logits_indices
1488
            num_sampled_tokens = num_draft_tokens + 1
1489
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1490
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1491
1492
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1493

1494
1495
1496
1497
1498
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1499
            )
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1511
        num_tokens: int,
1512
        num_reqs: int,
1513
1514
1515
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1516
1517
1518
1519
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1520
        num_scheduled_tokens: dict[str, int] | None = None,
1521
1522
1523
1524
1525
        cascade_attn_prefix_lens: list[list[int]] | None = None,
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1526
1527
1528
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs

1529
        logits_indices_padded = None
1530
        num_logits_indices = None
1531
1532
1533
1534
1535
1536
        if logits_indices is not None:
            num_logits_indices = logits_indices.size(0)
            if self.cache_config.kv_sharing_fast_prefill:
                logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                    logits_indices
                )
1537

1538
1539
1540
1541
1542
1543
        # update seq_lens of decode reqs under DCP.
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
                self.seq_lens.cpu[:num_reqs],
                self.dcp_world_size,
                self.dcp_rank,
1544
                self.parallel_config.cp_kv_cache_interleave_size,
1545
            )
1546
1547
            self.dcp_local_seq_lens.cpu[num_reqs:].fill_(0)
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs_padded)
1548

1549
1550
1551
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1552

1553
1554
1555
1556
1557
1558
1559
1560
        if for_cudagraph_capture:
            # For some attention backends (e.g. FA) with sliding window models we need
            # to make sure the backend see a max_seq_len that is larger to the sliding
            # window size when capturing to make sure the correct kernel is selected.
            max_seq_len = self.max_model_len
        else:
            max_seq_len = self.seq_lens.np[:num_reqs].max().item()

1561
1562
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1563
1564
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1565
1566
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1567

1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
        # Used in the below loop, uses padded shapes
        query_start_loc = self.query_start_loc.gpu[: num_reqs_padded + 1]
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs_padded + 1]
        seq_lens = self.seq_lens.gpu[:num_reqs_padded]
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs_padded]
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs_padded
        ]

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

        spec_decode_common_attn_metadata = None

1584
1585
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1586
        for kv_cache_gid, kv_cache_group in enumerate(
1587
1588
            self.kv_cache_config.kv_cache_groups
        ):
1589
1590
            encoder_seq_lens, encoder_seq_lens_cpu = self._get_encoder_seq_lens(
                num_scheduled_tokens or {},
1591
                kv_cache_group.kv_cache_spec,
1592
                num_reqs_padded,
1593
            )
1594

1595
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1596
1597
1598
                # 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(
1599
                    (num_reqs_padded, 1),
1600
                    dtype=torch.int32,
1601
1602
1603
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1604
                    (num_tokens_padded,),
1605
1606
1607
                    dtype=torch.int64,
                    device=self.device,
                )
1608
            else:
1609
                blk_table = self.input_batch.block_table[kv_cache_gid]
1610
1611
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
                slot_mapping = blk_table.slot_mapping.gpu[:num_tokens_padded]
1612
1613

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
1614
1615
1616
                # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
                slot_mapping[num_tokens:num_tokens_padded].fill_(-1)
                blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
1617

1618
            common_attn_metadata = CommonAttentionMetadata(
1619
1620
1621
1622
1623
                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,
1624
1625
1626
                num_actual_tokens=num_tokens_padded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
1627
                max_seq_len=max_seq_len,
1628
1629
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1630
                logits_indices_padded=logits_indices_padded,
1631
                num_logits_indices=num_logits_indices,
1632
                causal=True,
1633
                encoder_seq_lens=encoder_seq_lens,
1634
                encoder_seq_lens_cpu=encoder_seq_lens_cpu,
1635
                dcp_local_seq_lens=dcp_local_seq_lens,
1636
                dcp_local_seq_lens_cpu=dcp_local_seq_lens_cpu,
1637
1638
            )

1639
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1640
                if isinstance(self.drafter, EagleProposer):
1641
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1642
1643
1644
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1645

1646
1647
1648
1649
1650
1651
            for attn_gid, attn_group in enumerate(self.attn_groups[kv_cache_gid]):
                cascade_attn_prefix_len = (
                    cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                    if cascade_attn_prefix_lens
                    else 0
                )
1652
                builder = attn_group.get_metadata_builder()
1653

1654
                extra_attn_metadata_args = {}
1655
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1656
                    extra_attn_metadata_args = dict(
1657
1658
1659
                        num_accepted_tokens=self.num_accepted_tokens.gpu[
                            :num_reqs_padded
                        ],
1660
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
1661
                            :num_reqs_padded
1662
                        ],
1663
1664
                    )

1665
1666
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1667
1668
                        ubatch_slices, common_attn_metadata
                    )
1669
                    for ubid, common_attn_metadata in enumerate(
1670
1671
                        common_attn_metadata_list
                    ):
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
                        builder = attn_group.get_metadata_builder(ubatch_id=ubid)
                        if for_cudagraph_capture:
                            attn_metadata_i = builder.build_for_cudagraph_capture(
                                common_attn_metadata
                            )
                        else:
                            attn_metadata_i = builder.build(
                                common_prefix_len=cascade_attn_prefix_len,
                                common_attn_metadata=common_attn_metadata,
                            )
                        for layer_name in kv_cache_group.layer_names:
1683
1684
1685
1686
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
                    if for_cudagraph_capture:
                        attn_metadata_i = builder.build_for_cudagraph_capture(
                            common_attn_metadata
                        )
                    else:
                        attn_metadata_i = builder.build(
                            common_prefix_len=cascade_attn_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                            **extra_attn_metadata_args,
                        )
1697
1698
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1699

1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
        if spec_decode_common_attn_metadata is not None and (
            num_reqs != num_reqs_padded or num_tokens != num_tokens_padded
        ):
            # Currently the drafter still only uses piecewise cudagraphs (and modifies
            # the attention metadata in directly), and therefore does not want to use
            # padded attention metadata.
            spec_decode_common_attn_metadata = (
                spec_decode_common_attn_metadata.unpadded(num_tokens, num_reqs)
            )

1710
        return attn_metadata, spec_decode_common_attn_metadata
1711

1712
1713
1714
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
1715
        num_computed_tokens: np.ndarray,
1716
1717
1718
1719
1720
1721
1722
        num_common_prefix_blocks: list[int],
    ) -> list[list[int]] | None:
        """
        :return: Optional[cascade_attn_prefix_lens]
            cascade_attn_prefix_lens is 2D: ``[kv_cache_group_id][attn_group_idx]``,
            None if we should not use cascade attention
        """
1723

1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
        use_cascade_attn = False
        num_kv_cache_groups = len(self.kv_cache_config.kv_cache_groups)
        cascade_attn_prefix_lens: list[list[int]] = [
            [] for _ in range(num_kv_cache_groups)
        ]

        for kv_cache_gid in range(num_kv_cache_groups):
            for attn_group in self.attn_groups[kv_cache_gid]:
                if isinstance(attn_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                    cascade_attn_prefix_len = 0
                else:
                    # 0 if cascade attention should not be used
                    cascade_attn_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1738
                        num_computed_tokens,
1739
1740
1741
1742
1743
1744
1745
1746
                        num_common_prefix_blocks[kv_cache_gid],
                        attn_group.kv_cache_spec,
                        attn_group.get_metadata_builder(),
                    )
                cascade_attn_prefix_lens[kv_cache_gid].append(cascade_attn_prefix_len)
                use_cascade_attn |= cascade_attn_prefix_len > 0

        return cascade_attn_prefix_lens if use_cascade_attn else None
1747

1748
1749
1750
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
1751
        num_computed_tokens: np.ndarray,
1752
        num_common_prefix_blocks: int,
1753
1754
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
    ) -> 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.
        """
1773

1774
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
        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]
1812
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1813
1814
1815
1816
1817
        # 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.
1818
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
1819
        # common_prefix_len should be a multiple of the block size.
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
        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
        )
1831
1832
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1833
1834
1835
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1836
            num_kv_heads=kv_cache_spec.num_kv_heads,
1837
            use_alibi=self.use_alibi,
1838
            use_sliding_window=use_sliding_window,
1839
            use_local_attention=use_local_attention,
1840
            num_sms=self.num_sms,
1841
            dcp_world_size=self.dcp_world_size,
1842
1843
1844
        )
        return common_prefix_len if use_cascade else 0

1845
1846
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1847
        for index, req_id in enumerate(self.input_batch.req_ids):
1848
1849
1850
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1851
1852
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1853
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1854
1855
                req.prompt_token_ids, req.prompt_embeds
            )
1856
1857

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1858
1859
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
            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

1873
1874
1875
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1876
1877
1878
1879
1880
1881
1882
                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

1883
                assert req.mrope_position_delta is not None
1884
                MRotaryEmbedding.get_next_input_positions_tensor(
1885
                    out=self.mrope_positions.np,
1886
1887
1888
1889
1890
                    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,
                )
1891
1892
1893

                mrope_pos_ptr += completion_part_len

1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
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1912
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1918
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1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
    def _calc_xdrope_positions(self, scheduler_output: "SchedulerOutput"):
        xdrope_pos_ptr = 0
        for index, req_id in enumerate(self.input_batch.req_ids):
            req = self.requests[req_id]
            assert req.xdrope_positions is not None

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

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
            else:
                prompt_part_len = num_scheduled_tokens
                completion_part_len = 0

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's xdrope_positions are pre-computed
                dst_start = xdrope_pos_ptr
                dst_end = xdrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

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

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

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

                xdrope_pos_ptr += completion_part_len

1941
1942
    def _calc_spec_decode_metadata(
        self,
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
        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
1959
1960
1961
1962

        # 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(
1963
1964
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1965
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1966
        logits_indices = np.repeat(
1967
1968
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1969
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1970
1971
1972
1973
1974
1975
        logits_indices += arange

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

        # Compute the draft logits indices.
1976
1977
1978
        # 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(
1979
1980
            num_draft_tokens, cumsum_dtype=np.int32
        )
1981
1982
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1983
1984
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1985
1986
1987
1988
1989
        # [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(
1990
1991
            self.device, non_blocking=True
        )
1992
1993
1994
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1995
1996
1997
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1998
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1999
2000
            self.device, non_blocking=True
        )
2001
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2002
2003
            self.device, non_blocking=True
        )
2004

2005
2006
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2007
        draft_token_ids = self.input_ids.gpu[logits_indices]
2008
2009
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2010
        return SpecDecodeMetadata(
2011
2012
2013
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2014
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2015
2016
2017
2018
2019
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2020
2021
2022
2023
2024
2025
2026
    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
2027
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2028
2029
2030
2031
2032
        # 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_(
2033
2034
2035
2036
2037
2038
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
2039
2040
2041
2042
2043
            # 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
2044
2045
2046
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2047
2048
        return logits_indices_padded

2049
2050
2051
2052
2053
2054
2055
2056
    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
2057
                inputs.
2058
2059
2060
2061
2062
2063

        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
        """
2064
2065
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2066
            return [], []
2067
        # Batch the multi-modal inputs.
2068
        mm_kwargs = list[MultiModalKwargsItem]()
2069
2070
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
2071
2072
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2073
2074

            for mm_input_id in encoder_input_ids:
2075
                mm_feature = req_state.mm_features[mm_input_id]
2076
2077
                if mm_feature.data is None:
                    continue
2078
2079
2080
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
2081

2082
2083
        return mm_kwargs, mm_hashes_pos

2084
2085
2086
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2087
2088
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
2089
2090
            scheduler_output
        )
2091
2092

        if not mm_kwargs:
2093
            return []
2094

2095
2096
2097
2098
2099
2100
2101
        # 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.
2102
        model = cast(SupportsMultiModal, self.model)
2103
        encoder_outputs: list[torch.Tensor] = []
2104
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
2105
2106
2107
2108
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
2109
            multimodal_cpu_fields=model.multimodal_cpu_fields,
2110
        ):
2111
            curr_group_outputs: list[torch.Tensor] = []
2112
2113

            # EVS-related change.
2114
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2115
            # processing multimodal data. This solves the issue with scheduler
2116
2117
2118
2119
            # 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)
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
            # 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,
2136
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
2137
                        )
2138
                    )
2139

2140
                    micro_batch_outputs = model.embed_multimodal(
2141
2142
                        **micro_batch_mm_inputs
                    )
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152

                    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.
2153
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)  # type: ignore[assignment]
2154

2155
2156
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
2157
                expected_num_items=num_items,
2158
            )
2159
            encoder_outputs.extend(curr_group_outputs)
2160

2161
2162
2163
        # 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(
2164
2165
2166
                output,
                is_embed=pos_info.is_embed,
            )
2167
2168
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2169

2170
2171
        return encoder_outputs

2172
    def _gather_mm_embeddings(
2173
2174
        self,
        scheduler_output: "SchedulerOutput",
2175
        shift_computed_tokens: int = 0,
2176
2177
2178
2179
2180
2181
2182
2183
    ) -> 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
2184
        should_sync_mrope_positions = False
2185
        should_sync_xdrope_positions = False
2186

2187
        for req_id in self.input_batch.req_ids:
2188
2189
            mm_embeds_req: list[torch.Tensor] = []

2190
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2191
            req_state = self.requests[req_id]
2192
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2193

2194
2195
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2196
2197
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213

                # 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,
2214
2215
                    num_encoder_tokens,
                )
2216
                assert start_idx < end_idx
2217

2218
                mm_hash = mm_feature.identifier
2219
                encoder_output = self.encoder_cache.get(mm_hash, None)
2220
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2221
2222
2223
2224

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

2225
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2226
2227
2228
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
2229

2230
2231
2232
2233
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
2234
2235
2236
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2237
                assert req_state.mrope_positions is not None
2238
2239
2240
2241
2242
2243
2244
                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,
2245
2246
                    )
                )
2247
2248
2249
2250
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2251
2252
2253
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
2254
2255
2256

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2257
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2258

2259
2260
2261
2262
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

2263
        return mm_embeds, is_mm_embed
2264

2265
    def get_model(self) -> nn.Module:
2266
        # get raw model out of the cudagraph wrapper.
2267
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2268
            return self.model.unwrap()
2269
2270
        return self.model

2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
    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

2286
2287
2288
2289
2290
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2291
2292
        supported_tasks = list(model.pooler.get_supported_tasks())

2293
        if self.scheduler_config.enable_chunked_prefill:
2294
2295
2296
2297
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2298

2299
2300
            logger.debug_once(
                "Chunked prefill is not supported with "
2301
2302
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2303
2304
2305
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2306
2307
2308
2309
2310

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2311
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2312
2313

        return supported_tasks
2314

2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
    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)

2325
    def sync_and_slice_intermediate_tensors(
2326
2327
        self,
        num_tokens: int,
2328
        intermediate_tensors: IntermediateTensors | None,
2329
2330
        sync_self: bool,
    ) -> IntermediateTensors:
2331
2332
2333
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2334
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2335
2336
2337
2338
2339
2340

        # 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():
2341
                is_scattered = k == "residual" and is_rs
2342
                copy_len = num_tokens // tp if is_scattered else num_tokens
2343
                self.intermediate_tensors[k][:copy_len].copy_(
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
                    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:
2357
2358
2359
2360
2361
2362
2363
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2364
2365
        model = self.get_model()
        assert is_mixture_of_experts(model)
2366
2367
2368
        self.eplb_state.step(
            is_dummy,
            is_profile,
2369
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2370
2371
        )

2372
2373
2374
2375
2376
2377
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2378
2379
2380
        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"
        )
2381

2382
        hidden_states = hidden_states[:num_scheduled_tokens]
2383
        pooling_metadata = self.input_batch.get_pooling_metadata()
2384
2385
2386
2387
        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]
2388

2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
        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()
2399

2400
        pooler_output: list[torch.Tensor | None] = []
2401
        for raw_output, seq_len, prompt_len in zip(
2402
2403
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2404
            output = raw_output if seq_len == prompt_len else None
2405
            pooler_output.append(output)
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415

        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,
        )

2416
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2417
2418
2419
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2420
2421
2422
2423
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2424
2425
2426
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2427
    def _preprocess(
2428
2429
        self,
        scheduler_output: "SchedulerOutput",
2430
        num_input_tokens: int,  # Padded
2431
        intermediate_tensors: IntermediateTensors | None = None,
2432
    ) -> tuple[
2433
2434
        torch.Tensor | None,
        torch.Tensor | None,
2435
        torch.Tensor,
2436
        IntermediateTensors | None,
2437
        dict[str, Any],
2438
        ECConnectorOutput | None,
2439
    ]:
2440
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2441
        is_first_rank = get_pp_group().is_first_rank
2442
        is_encoder_decoder = self.model_config.is_encoder_decoder
2443

2444
2445
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2446
2447
        ec_connector_output = None

2448
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
2449
            # Run the multimodal encoder if any.
2450
2451
2452
2453
2454
2455
            with self.maybe_get_ec_connector_output(
                scheduler_output,
                encoder_cache=self.encoder_cache,
            ) as ec_connector_output:
                self._execute_mm_encoder(scheduler_output)
                mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2456

2457
2458
2459
            # 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.
2460
            inputs_embeds_scheduled = self.model.embed_input_ids(
2461
2462
2463
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2464
            )
2465

2466
            # TODO(woosuk): Avoid the copy. Optimize.
2467
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2468

2469
            input_ids = None
2470
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2471
2472
2473
2474
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2475
        elif self.enable_prompt_embeds and is_first_rank:
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
            # 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).
2488
2489
2490
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2491
                .squeeze(1)
2492
            )
2493
2494
2495
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2496
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2497
2498
2499
2500
2501
                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
2502
        else:
2503
2504
2505
2506
            # 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.
2507
            input_ids = self.input_ids.gpu[:num_input_tokens]
2508
            inputs_embeds = None
2509
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2510

2511
        if self.uses_mrope:
2512
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2513
2514
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
2515
        else:
2516
            positions = self.positions.gpu[:num_input_tokens]
2517

2518
        if is_first_rank:
2519
2520
            intermediate_tensors = None
        else:
2521
            assert intermediate_tensors is not None
2522
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2523
2524
                num_input_tokens, intermediate_tensors, True
            )
2525

2526
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
2527
2528
2529
2530
2531
2532
2533
            # Run the encoder, just like we do with other multimodal inputs.
            # For an encoder-decoder model, our processing here is a bit
            # simpler, because the outputs are just passed to the decoder.
            # We are not doing any prompt replacement. We also will only
            # ever have a single encoder input.
            encoder_outputs = self._execute_mm_encoder(scheduler_output)
            model_kwargs.update({"encoder_outputs": encoder_outputs})
2534

2535
2536
2537
2538
2539
2540
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2541
            ec_connector_output,
2542
        )
2543

2544
    def _sample(
2545
        self,
2546
2547
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2548
    ) -> SamplerOutput:
2549
        # Sample the next token and get logprobs if needed.
2550
        sampling_metadata = self.input_batch.sampling_metadata
2551
        if spec_decode_metadata is None:
2552
2553
2554
            # 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()
2555
            return self.sampler(
2556
2557
2558
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2559

2560
        sampler_output = self.rejection_sampler(
2561
2562
            spec_decode_metadata,
            None,  # draft_probs
2563
            logits,
2564
2565
            sampling_metadata,
        )
2566
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2567
2568
2569
        return sampler_output

    def _bookkeeping_sync(
2570
2571
2572
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2573
        logits: torch.Tensor | None,
2574
2575
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2576
        spec_decode_metadata: SpecDecodeMetadata | None,
2577
    ) -> tuple[
2578
        dict[str, int],
2579
        LogprobsLists | None,
2580
        list[list[int]],
2581
        dict[str, LogprobsTensors | None],
2582
2583
2584
        list[str],
        dict[str, int],
        list[int],
2585
    ]:
2586
2587
2588
2589
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2590
2591
2592
2593
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
2594
2595
2596
2597
        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)
2598

2599
2600
2601
        # 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()
2602
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2603
2604

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2605
        sampled_token_ids = sampler_output.sampled_token_ids
2606
        logprobs_tensors = sampler_output.logprobs_tensors
2607
        invalid_req_indices = []
2608
        cu_num_tokens: list[int] | None = None
2609
2610
2611
2612
2613
2614
        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)
2615
2616
2617
                # Mask out the sampled tokens that should not be sampled.
                for i in discard_sampled_tokens_req_indices:
                    valid_sampled_token_ids[int(i)].clear()
2618
2619
            else:
                # Includes spec decode tokens.
2620
                valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
2621
2622
                    sampled_token_ids,
                    self.input_batch.vocab_size,
2623
2624
                    discard_sampled_tokens_req_indices,
                    return_cu_num_tokens=logprobs_tensors is not None,
2625
                )
2626
        else:
2627
            valid_sampled_token_ids = []
2628
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2629
2630
2631
2632
2633
            invalid_req_indices_set = set(invalid_req_indices)

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
2634
2635
2636
2637
            # With spec decoding, this is done in propose_draft_token_ids().
            if self.input_batch.prev_sampled_token_ids is None:
                assert sampled_token_ids.shape[-1] == 1
                self.input_batch.prev_sampled_token_ids = sampled_token_ids
2638
2639
2640
2641
2642
            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
            }
2643

2644
2645
2646
2647
2648
        # 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.
2649
        req_ids = self.input_batch.req_ids
2650
2651
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2652
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2653
2654
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2655

2656
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
2657

2658
            if not sampled_ids:
2659
2660
2661
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2662
            end_idx = start_idx + num_sampled_ids
2663
2664
2665
2666
            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}"
2667
            )
2668

2669
2670
            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
2671
2672
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2673

2674
            req_id = req_ids[req_idx]
2675
2676
2677
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2678
        logprobs_lists = (
2679
            logprobs_tensors.tolists(cu_num_tokens)
2680
            if not self.use_async_scheduling and logprobs_tensors is not None
2681
2682
2683
2684
2685
2686
2687
2688
2689
            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,
        )

2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
        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,
        )

2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
    @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()

2715
2716
    def _model_forward(
        self,
2717
2718
2719
2720
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2721
2722
2723
2724
2725
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2726
        Motivation: We can inspect only this method versus
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
        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,
        )

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

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

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

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

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
        ubatch_slices, num_tokens_across_dp = None, None
        if self.vllm_config.parallel_config.data_parallel_size > 1:
            # Disable DP padding when running eager to avoid excessive padding when
            # running prefills. This lets us set cudagraph_mode="NONE" on the prefiller
            # in a P/D setup and still use CUDA graphs (enabled by this padding) on the
            # decoder.
            allow_dp_padding = (
                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            )

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

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

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

        return cudagraph_mode, batch_descriptor, ubatch_slices, num_tokens_across_dp

2828
2829
2830
2831
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2832
        intermediate_tensors: IntermediateTensors | None = None,
2833
2834
2835
2836
2837
2838
    ) -> 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."
            )
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853

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

2854
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2855
        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
2856
2857
2858
2859
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

2860
2861
2862
2863
2864
2865
2866
2867
                if has_ec_transfer() and get_ec_transfer().is_producer:
                    with self.maybe_get_ec_connector_output(
                        scheduler_output,
                        encoder_cache=self.encoder_cache,
                    ) as ec_connector_output:
                        self._execute_mm_encoder(scheduler_output)
                        return make_empty_encoder_model_runner_output(scheduler_output)

2868
                if not num_scheduled_tokens:
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
                    if (
                        self.parallel_config.distributed_executor_backend
                        == "external_launcher"
                        and self.parallel_config.data_parallel_size > 1
                    ):
                        # this is a corner case when both external launcher
                        # and DP are enabled, num_scheduled_tokens could be
                        # 0, and has_unfinished_requests in the outer loop
                        # returns True. before returning early here we call
                        # dummy run to ensure coordinate_batch_across_dp
                        # is called into to avoid out of sync issues.
2880
                        self._dummy_run(1)
2881
2882
2883
2884
                    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(
2885
2886
                        scheduler_output, self.vllm_config
                    )
2887
                if self.cache_config.kv_sharing_fast_prefill:
2888
                    assert not self.num_prompt_logprobs, (
2889
2890
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2891
2892
                        "it when the requests need prompt logprobs"
                    )
2893

2894
2895
2896
2897
2898
                num_reqs = self.input_batch.num_reqs
                req_ids = self.input_batch.req_ids
                tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
                num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
                max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())
2899
                num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
2900

2901
2902
2903
                (
                    logits_indices,
                    spec_decode_metadata,
2904
                ) = self._prepare_inputs(
2905
2906
                    scheduler_output,
                    num_scheduled_tokens_np,
2907
2908
2909
2910
                )

                cascade_attn_prefix_lens = None
                # Disable cascade attention when using microbatching (DBO)
2911
                if self.cascade_attn_enabled and not self.parallel_config.enable_dbo:
2912
2913
2914
                    # Pre-compute cascade attention prefix lengths
                    cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
                        num_scheduled_tokens_np,
2915
                        self.input_batch.num_computed_tokens_cpu[:num_reqs],
2916
2917
2918
                        scheduler_output.num_common_prefix_blocks,
                    )

2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
                (
                    cudagraph_mode,
                    batch_desc,
                    ubatch_slices,
                    num_tokens_across_dp,
                ) = self._determine_batch_execution_and_padding(
                    num_tokens=num_tokens_unpadded,
                    num_reqs=num_reqs,
                    num_scheduled_tokens_np=num_scheduled_tokens_np,
                    max_num_scheduled_tokens=max_num_scheduled_tokens,
                    use_cascade_attn=cascade_attn_prefix_lens is not None,
                )

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

                num_tokens_padded = batch_desc.num_tokens
                num_reqs_padded = (
                    batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
                )
2945
2946

                use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2947
2948
2949
                pad_attn = cudagraph_mode == CUDAGraphMode.FULL

                (attn_metadata, spec_decode_common_attn_metadata) = (
2950
                    self._build_attention_metadata(
2951
2952
                        num_tokens=num_tokens_unpadded,
                        num_tokens_padded=num_tokens_padded if pad_attn else None,
2953
                        num_reqs=num_reqs,
2954
2955
                        num_reqs_padded=num_reqs_padded if pad_attn else None,
                        max_query_len=max_num_scheduled_tokens,
2956
2957
2958
                        ubatch_slices=ubatch_slices,
                        logits_indices=logits_indices,
                        use_spec_decode=use_spec_decode,
2959
                        num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
2960
2961
2962
                        cascade_attn_prefix_lens=cascade_attn_prefix_lens,
                    )
                )
2963

2964
2965
2966
2967
2968
2969
2970
2971
2972
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
2973
            )
2974

2975
        # Set cudagraph mode to none if calc_kv_scales is true.
2976
2977
2978
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
2979
            cudagraph_mode = CUDAGraphMode.NONE
2980
2981
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
2982

2983
2984
        # Run the model.
        # Use persistent buffers for CUDA graphs.
2985
2986
        with (
            set_forward_context(
2987
2988
                attn_metadata,
                self.vllm_config,
2989
                num_tokens=num_tokens_padded,
2990
                num_tokens_across_dp=num_tokens_across_dp,
2991
2992
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
2993
                ubatch_slices=ubatch_slices,
2994
            ),
2995
            record_function_or_nullcontext("gpu_model_runner: forward"),
2996
2997
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2998
            model_output = self._model_forward(
2999
3000
3001
3002
3003
3004
3005
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3006
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3007
            if self.use_aux_hidden_state_outputs:
3008
                # True when EAGLE 3 is used.
3009
3010
                hidden_states, aux_hidden_states = model_output
            else:
3011
                # Common case.
3012
3013
3014
                hidden_states = model_output
                aux_hidden_states = None

3015
3016
3017
3018
3019
            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)
3020
                    hidden_states.kv_connector_output = kv_connector_output
3021
                    self.kv_connector_output = kv_connector_output
3022
                    return hidden_states
3023

3024
                if self.is_pooling_model:
3025
                    # Return the pooling output.
3026
3027
3028
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
3029
3030
                    output.kv_connector_output = kv_connector_output
                    return output
3031
3032

                sample_hidden_states = hidden_states[logits_indices]
3033
                logits = self.model.compute_logits(sample_hidden_states)
3034
3035
3036
3037
            else:
                # Rare case.
                assert not self.is_pooling_model

3038
                sample_hidden_states = hidden_states[logits_indices]
3039
                if not get_pp_group().is_last_rank:
3040
                    all_gather_tensors = {
3041
                        "residual": not is_residual_scattered_for_sp(
3042
                            self.vllm_config, num_tokens_padded
3043
                        )
3044
                    }
3045
                    get_pp_group().send_tensor_dict(
3046
3047
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3048
3049
                        all_gather_tensors=all_gather_tensors,
                    )
3050
3051
                    logits = None
                else:
3052
                    logits = self.model.compute_logits(sample_hidden_states)
3053

3054
                model_output_broadcast_data: dict[str, Any] = {}
3055
3056
3057
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3058
                broadcasted = get_pp_group().broadcast_tensor_dict(
3059
3060
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3061
3062
                assert broadcasted is not None
                logits = broadcasted["logits"]
3063

3064
3065
3066
3067
3068
3069
3070
3071
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3072
            ec_connector_output,
3073
        )
3074
        self.kv_connector_output = kv_connector_output
3075
3076
3077
3078
3079
3080
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
3081
3082
3083
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

3084
3085
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
3086
            if not kv_connector_output:
3087
                return None  # type: ignore[return-value]
3088
3089
3090
3091
3092
3093
3094
3095
3096

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

            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
            output.kv_connector_output = kv_connector_output
            return output
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3107
            ec_connector_output,
3108
3109
3110
3111
3112
3113
3114
3115
3116
        ) = 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
            )
3117

3118
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3119
3120
            sampler_output = self._sample(logits, spec_decode_metadata)

3121
3122
        self.input_batch.prev_sampled_token_ids = None

3123
        def propose_draft_token_ids(sampled_token_ids):
3124
            assert spec_decode_common_attn_metadata is not None
3125
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
                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,
                )

3137
        spec_config = self.speculative_config
3138
        use_padded_batch_for_eagle = (
3139
3140
3141
            spec_config is not None
            and spec_config.use_eagle()
            and not spec_config.disable_padded_drafter_batch
3142
        )
3143
3144
3145
        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
3146
        if (
3147
3148
3149
            spec_config is not None
            and spec_config.draft_model_config is not None
            and spec_config.draft_model_config.max_model_len is not None
3150
        ):
3151
            effective_drafter_max_model_len = (
3152
                spec_config.draft_model_config.max_model_len
3153
            )
3154
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
3155
            spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
3156
3157
            <= effective_drafter_max_model_len
        )
3158
        if use_padded_batch_for_eagle:
3159
3160
            assert self.speculative_config is not None
            assert isinstance(self.drafter, EagleProposer)
3161
3162
3163
3164
3165
3166
            sampled_token_ids = sampler_output.sampled_token_ids
            if input_fits_in_drafter:
                # EAGLE speculative decoding can use the GPU sampled tokens
                # as inputs, and does not need to wait for bookkeeping to finish.
                propose_draft_token_ids(sampled_token_ids)
            elif self.valid_sampled_token_count_event is not None:
3167
                assert spec_decode_common_attn_metadata is not None
3168
3169
3170
3171
3172
3173
                next_token_ids, valid_sampled_tokens_count = (
                    self.drafter.prepare_next_token_ids_padded(
                        spec_decode_common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
3174
                        self.discard_request_mask.gpu,
3175
3176
3177
3178
3179
                    )
                )
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
3180

3181
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
3182
3183
3184
3185
3186
3187
3188
3189
            (
                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,
3190
3191
3192
3193
3194
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
3195
                scheduler_output.total_num_scheduled_tokens,
3196
                spec_decode_metadata,
3197
            )
3198

3199
3200
3201
3202
3203
        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
3204
3205
3206
            # 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)
3207

3208
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
3209
            self.eplb_step()
3210
3211
3212
3213
3214
3215
3216
3217
3218
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
            output = ModelRunnerOutput(
                req_ids=req_ids_output_copy,
                req_id_to_index=req_id_to_index_output_copy,
                sampled_token_ids=valid_sampled_token_ids,
                logprobs=logprobs_lists,
                prompt_logprobs_dict=prompt_logprobs_dict,
                pooler_output=[],
                kv_connector_output=kv_connector_output,
3219
3220
3221
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
3222
3223
                num_nans_in_logits=num_nans_in_logits,
            )
3224

3225
3226
        if not self.use_async_scheduling:
            return output
3227
3228
3229
3230
3231
3232
3233
3234
3235
        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
                async_output_copy_stream=self.async_output_copy_stream,
3236
                vocab_size=self.input_batch.vocab_size,
3237
3238
3239
3240
3241
            )
        with record_function_or_nullcontext(
            "gpu_model_runner: set_async_sampled_token_ids"
        ):
            # Save ref of sampled_token_ids CPU tensor if the batch contains
3242
            # any requests with sampling params that require output ids.
3243
3244
3245
3246
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
3247
3248
3249

        return async_output

3250
    def take_draft_token_ids(self) -> DraftTokenIds | None:
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
        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)

3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
    def _copy_valid_sampled_token_count(
        self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
    ) -> None:
        if self.valid_sampled_token_count_event is None:
            return

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

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

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

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

3292
3293
3294
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
3295
        sampled_token_ids: torch.Tensor | list[list[int]],
3296
3297
3298
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
3299
3300
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3301
        common_attn_metadata: CommonAttentionMetadata,
3302
    ) -> list[list[int]] | torch.Tensor:
3303
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3304
3305
3306
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
3307
            assert isinstance(sampled_token_ids, list)
3308
            assert isinstance(self.drafter, NgramProposer)
3309
            draft_token_ids = self.drafter.propose(
3310
3311
                sampled_token_ids,
                self.input_batch.req_ids,
3312
3313
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
3314
3315
                self.input_batch.spec_decode_unsupported_reqs,
            )
3316
        elif spec_config.method == "suffix":
3317
3318
3319
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
3320
        elif spec_config.method == "medusa":
3321
            assert isinstance(sampled_token_ids, list)
3322
            assert isinstance(self.drafter, MedusaProposer)
3323

3324
3325
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
3326
3327
3328
3329
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
3330
3331
3332
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
3333
                for num_draft, tokens in zip(
3334
3335
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
3336
                    indices.append(offset + len(tokens) - 1)
3337
                    offset += num_draft + 1
3338
                indices = torch.tensor(indices, device=self.device)
3339
3340
                hidden_states = sample_hidden_states[indices]

3341
            draft_token_ids = self.drafter.propose(
3342
3343
3344
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
3345
        elif spec_config.use_eagle():
3346
            assert isinstance(self.drafter, EagleProposer)
3347

3348
            if spec_config.disable_padded_drafter_batch:
3349
3350
3351
                # 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.
3352
3353
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
3354
                    "padded-batch is disabled."
3355
                )
3356
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
3357
3358
3359
3360
3361
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
3362
3363
3364
3365
3366
            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.
3367
3368
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
3369
                    "padded-batch is enabled."
3370
3371
                )
                next_token_ids, valid_sampled_tokens_count = (
3372
3373
3374
3375
3376
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
3377
                        self.discard_request_mask.gpu,
3378
                    )
3379
                )
3380
3381
3382
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
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Jiayi Yao committed
3383

3384
            if spec_decode_metadata is None:
3385
                token_indices_to_sample = None
3386
                # input_ids can be None for multimodal models.
3387
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
3388
                target_positions = self._get_positions(num_scheduled_tokens)
3389
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3390
                    assert aux_hidden_states is not None
3391
                    target_hidden_states = torch.cat(
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                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
3396
            else:
3397
                if spec_config.disable_padded_drafter_batch:
3398
                    token_indices_to_sample = None
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                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
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                    target_token_ids = self.input_ids.gpu[token_indices]
                    target_positions = self._get_positions(token_indices)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[token_indices] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[token_indices]
3413
                else:
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                    common_attn_metadata, token_indices_to_sample = (
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                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
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                            valid_sampled_tokens_count,
                        )
                    )
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                    total_num_tokens = common_attn_metadata.num_actual_tokens
                    # When padding the batch, token_indices is just a range
                    target_token_ids = self.input_ids.gpu[:total_num_tokens]
                    target_positions = self._get_positions(total_num_tokens)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[:total_num_tokens] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[:total_num_tokens]
3432

3433
            if self.supports_mm_inputs:
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
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3441
            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
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                last_token_indices=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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                mm_embed_inputs=mm_embed_inputs,
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            )
3451

3452
        return draft_token_ids
3453

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    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
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            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
3459
                f"Allowed configs: {allowed_config_names}"
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            )
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

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    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
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        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
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        global_expert_loads, old_global_expert_indices_per_model, rank_mapping = (
            EplbState.get_eep_state(self.parallel_config)
            if eep_scale_up
            else (None, None, None)
        )
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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
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        with DeviceMemoryProfiler() as m:
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            time_before_load = time.perf_counter()
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            model_loader = get_model_loader(self.load_config)
3487
            self.model = model_loader.load_model(
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                vllm_config=self.vllm_config, model_config=self.model_config
            )
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            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
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            if hasattr(self, "drafter"):
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                logger.info_once("Loading drafter model...")
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                self.drafter.load_model(self.model)
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                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
3502
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                    spec_config = self.vllm_config.speculative_config
                    assert spec_config is not None
                    assert spec_config.draft_model_config is not None
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                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
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                        spec_config.draft_model_config.model,
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                    )

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

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            if self.use_aux_hidden_state_outputs:
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                if not supports_eagle3(self.get_model()):
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                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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                        "aux_hidden_state_outputs was requested"
                    )
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                # 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)
3550
            time_after_load = time.perf_counter()
3551
        self.model_memory_usage = m.consumed_memory
3552
        logger.info_once(
3553
            "Model loading took %.4f GiB memory and %.6f seconds",
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            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3556
            scope="local",
3557
        )
3558
        prepare_communication_buffer_for_model(self.model)
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        if (drafter := getattr(self, "drafter", None)) and (
            drafter_model := getattr(drafter, "model", None)
        ):
            prepare_communication_buffer_for_model(drafter_model)
3563
        mm_config = self.model_config.multimodal_config
3564
        self.is_multimodal_pruning_enabled = (
3565
            supports_multimodal_pruning(self.get_model())
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3567
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
3568
        )
3569

3570
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
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            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            global_expert_load = (
                global_expert_loads[eplb_models] if global_expert_loads else None
            )
            old_global_expert_indices = (
                old_global_expert_indices_per_model[eplb_models]
                if old_global_expert_indices_per_model
                else None
            )
            assert self.eplb_state is not None
            self.eplb_state.add_model(
3582
                self.model,
3583
                self.model_config,
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                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3587
            )
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            if self.eplb_state.is_async:
                self.eplb_state.start_async_loop(rank_mapping=rank_mapping)
3590

3591
        if (
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3593
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3594
            and supports_dynamo()
3595
        ):
3596
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3597
            compilation_counter.stock_torch_compile_count += 1
3598
            self.model.compile(fullgraph=True, backend=backend)
3599
            return
3600
        # for other compilation modes, cudagraph behavior is controlled by
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        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
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        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
        if cudagraph_mode.has_full_cudagraphs() and not self.parallel_config.enable_dbo:
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            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3610
        elif self.parallel_config.enable_dbo:
3611
            if cudagraph_mode.has_full_cudagraphs():
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3615
            else:
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3619

3620
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
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        """Extract Eagle3 auxiliary layer indices from speculative config.

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

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

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

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

        return None

3644
    def reload_weights(self) -> None:
3645
        assert getattr(self, "model", None) is not None, (
3646
            "Cannot reload weights before model is loaded."
3647
        )
3648
3649
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3650
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3651

3652
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    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3657
            self.get_model(),
3658
            tensorizer_config=tensorizer_config,
3659
            model_config=self.model_config,
3660
3661
        )

3662
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    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3665
        num_scheduled_tokens: dict[str, int],
3666
    ) -> dict[str, LogprobsTensors | None]:
3667
        num_prompt_logprobs_dict = self.num_prompt_logprobs
3668
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3670
        if not num_prompt_logprobs_dict:
            return {}

3671
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3672
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3673
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3675
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3677

        # 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():
3678
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3681
            num_tokens = num_scheduled_tokens.get(req_id)
            if num_tokens is None:
                # This can happen if the request was preempted in prefill stage.
                continue
3682
3683
3684

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

3689
3690
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3691
3692
                self.device, non_blocking=True
            )
3693

3694
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            # 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(
3700
3701
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3702
3703
                in_progress_dict[req_id] = logprobs_tensors

3704
            # Determine number of logits to retrieve.
3705
3706
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3707
            num_remaining_tokens = num_prompt_tokens - start_tok
3708
            if num_tokens <= num_remaining_tokens:
3709
                # This is a chunk, more tokens remain.
3710
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                # 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.
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                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)
3718
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3720
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3724
                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
3725
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3729

            # 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]
3730
            offset = self.query_start_loc.np[req_idx].item()
3731
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3732
            logits = self.model.compute_logits(prompt_hidden_states)
3733
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3736

            # 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.
3737
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3738
3739

            # Compute prompt logprobs.
3740
3741
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3742
3743
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3744
3745

            # Transfer GPU->CPU async.
3746
3747
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3748
3749
3750
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3751
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3752
3753
                ranks, non_blocking=True
            )
3754
3755
3756
3757
3758

        # 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]
3759
            del in_progress_dict[req_id]
3760
3761

        # Must synchronize the non-blocking GPU->CPU transfers.
3762
        if prompt_logprobs_dict:
3763
            self._sync_device()
3764
3765
3766

        return prompt_logprobs_dict

3767
3768
    def _get_nans_in_logits(
        self,
3769
        logits: torch.Tensor | None,
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    ) -> 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])
3781
3782
3783
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3784
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3787
            return num_nans_in_logits
        except IndexError:
            return {}

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3793
    @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
3794
         - during DP rank dummy run
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        """
        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(
3806
                    self.input_ids.gpu,
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3808
                    low=0,
                    high=self.model_config.get_vocab_size(),
3809
3810
                    dtype=input_ids.dtype,
                )
3811

3812
            logger.debug_once("Randomizing dummy data for DP Rank")
3813
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3814
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3816
            yield
            input_ids.fill_(0)

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    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3823
3824
        assert self.mm_budget is not None

3825
3826
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3827
            seq_len=self.max_model_len,
3828
            mm_counts={modality: 1},
3829
            cache=self.mm_budget.cache,
3830
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3832
3833
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3834
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        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3836

3837
        model = cast(SupportsMultiModal, self.model)
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3844
        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,
3845
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3846
3847
            )
        )
3848

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3852
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3853
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
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3855
        force_attention: bool = False,
        uniform_decode: bool = False,
3856
        allow_microbatching: bool = True,
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3858
        skip_eplb: bool = False,
        is_profile: bool = False,
3859
        create_mixed_batch: bool = False,
3860
        remove_lora: bool = True,
3861
        activate_lora: bool = False,
Rémi Delacourt's avatar
Rémi Delacourt committed
3862
        is_graph_capturing: bool = False,
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    ) -> tuple[torch.Tensor, torch.Tensor]:
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        """
        Run a dummy forward pass to warm up/profile run or capture the
        CUDA graph for the model.

        Args:
            num_tokens: Number of tokens to run the dummy forward pass.
            cudagraph_runtime_mode: used to control the behavior.
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                - if not set will determine the cudagraph mode based on using
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                    the self.cudagraph_dispatcher.
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                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
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            force_attention: If True, always create attention metadata. Used to
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                warm up attention backend when mode is NONE.
            uniform_decode: If True, the batch is a uniform decode batch.
            skip_eplb: If True, skip EPLB state update.
            is_profile: If True, this is a profile run.
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            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
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            remove_lora: If False, dummy LoRAs are not destroyed after the run
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            activate_lora: If False, dummy_run is performed without LoRAs.
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        """
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        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
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        # If cudagraph_mode.decode_mode() == FULL and
3893
        # cudagraph_mode.separate_routine(). This means that we are using
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        # different graphs and/or modes for mixed prefill-decode batches vs.
        # uniform decode batches. A uniform decode batch means that all
        # requests have identical query length, except a potential virtual
        # request (shorter) in the batch account for padding.
        # Uniform decode batch could either be common pure decode, where
        # max_query_len == 1, or speculative decode, where
        # max_query_len == 1 + num_spec_decode_tokens.

        # When setting max_query_len = 1, we switch to and capture the optimized
        # routine of FA2 for pure decode, i.e., Flashdecode + an optimization
        # for GQA/MQA.
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        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3906

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        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
        # for dummy run with LoRA so that the num_reqs collectively
        # has num_tokens in total.
        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
        max_num_reqs = self.scheduler_config.max_num_seqs
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        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
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            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
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            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

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

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

3941
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3942

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

        num_tokens_padded = batch_desc.num_tokens
        num_reqs_padded = (
            batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
        )
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        attn_metadata: PerLayerAttnMetadata | None = None
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        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
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        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
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            if create_mixed_batch:
                # In the mixed batch mode (used for FI warmup), we use
                # shorter sequence lengths to run faster.
                # TODO(luka) better system for describing dummy batches
                seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]
            else:
3989
                seq_lens = max_query_len  # type: ignore[assignment]
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            self.seq_lens.np[:num_reqs] = seq_lens
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            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
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            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
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            self.query_start_loc.copy_to_gpu()

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            attn_metadata, _ = self._build_attention_metadata(
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                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
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                ubatch_slices=ubatch_slices,
                for_cudagraph_capture=True,
            )
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        with self.maybe_dummy_run_with_lora(
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            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
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        ):
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            # Make sure padding doesn't exceed max_num_tokens
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            assert num_tokens_padded <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_padded)
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            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
4017
                input_ids = None
4018
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
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                model_kwargs = {
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                    **model_kwargs,
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                    **self._dummy_mm_kwargs(num_reqs),
                }
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            elif self.enable_prompt_embeds:
                input_ids = None
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                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
                model_kwargs = self._init_model_kwargs(num_tokens_padded)
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            else:
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                input_ids = self.input_ids.gpu[:num_tokens_padded]
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                inputs_embeds = None
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            if self.uses_mrope:
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                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
4033
            elif self.uses_xdrope_dim > 0:
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                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
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            else:
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                positions = self.positions.gpu[:num_tokens_padded]
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            if get_pp_group().is_first_rank:
                intermediate_tensors = None
            else:
                if self.intermediate_tensors is None:
                    self.intermediate_tensors = (
                        self.model.make_empty_intermediate_tensors(
                            batch_size=self.max_num_tokens,
                            dtype=self.model_config.dtype,
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                            device=self.device,
                        )
                    )
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4050

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
4051
                    num_tokens_padded, None, False
4052
                )
4053

4054
            if ubatch_slices is not None:
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                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
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                num_tokens_padded = ubatch_slices[0].num_tokens
4059
                if num_tokens_across_dp is not None:
4060
                    num_tokens_across_dp[:] = num_tokens_padded
4061

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            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
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                    attn_metadata,
                    self.vllm_config,
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                    num_tokens=num_tokens_padded,
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                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
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                    batch_descriptor=batch_desc,
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                    ubatch_slices=ubatch_slices,
                ),
            ):
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                outputs = self.model(
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                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
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                    **model_kwargs,
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                )
4081

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4085
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
4086

4087
            if self.speculative_config and self.speculative_config.use_eagle():
4088
                assert isinstance(self.drafter, EagleProposer)
4089
                use_cudagraphs = (
Rémi Delacourt's avatar
Rémi Delacourt committed
4090
                    cudagraph_runtime_mode.has_mode(CUDAGraphMode.PIECEWISE)
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                    and not self.speculative_config.enforce_eager
                )
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                # Note(gnovack) - We need to disable cudagraphs for one of the two
                # lora cases when cudagraph_specialize_lora is enabled. This is a
                # short term mitigation for issue mentioned in
                # https://github.com/vllm-project/vllm/issues/28334
                if self.compilation_config.cudagraph_specialize_lora and activate_lora:
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
4104
                    is_graph_capturing=is_graph_capturing,
4105
                )
4106

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        # This is necessary to avoid blocking DP.
        # For dummy runs, we typically skip EPLB since we don't have any real
        # requests to process.
        # However, in DP settings, there may be cases when some DP ranks do
        # not have any requests to process, so they're executing dummy batches.
        # In such cases, we still have to trigger EPLB to make sure
        # ranks execute the rearrangement in synchronization.
        if not skip_eplb:
            self.eplb_step(is_dummy=True, is_profile=is_profile)

4117
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
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        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
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4127

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
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4131
        # 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)
4132

4133
        logits = self.model.compute_logits(hidden_states)
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4135
        num_reqs = logits.size(0)

4136
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
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4151

        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)],
4152
            spec_token_ids=[[] for _ in range(num_reqs)],
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4154
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
4155
            logitsprocs=LogitsProcessors(),
4156
        )
4157
        try:
4158
4159
4160
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
4161
        except RuntimeError as e:
4162
            if "out of memory" in str(e):
4163
4164
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4166
                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 "
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4168
                    "initializing the engine."
                ) from e
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4170
            else:
                raise e
4171
        if self.speculative_config:
4172
4173
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
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4175
                draft_token_ids, self.device
            )
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4179
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4181

            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
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            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
4187
            )
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            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
4191
                logits,
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4193
                dummy_metadata,
            )
4194
        return sampler_output
4195

4196
    def _dummy_pooler_run_task(
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        self,
        hidden_states: torch.Tensor,
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        task: PoolingTask,
    ) -> PoolerOutput:
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        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

4212
        dummy_prompt_lens = torch.tensor(
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4214
            num_scheduled_tokens_list,
            device="cpu",
4215
        )
4216
4217
4218
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
4219

4220
        model = cast(VllmModelForPooling, self.get_model())
4221
        dummy_pooling_params = PoolingParams(task=task)
4222
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
4223
        to_update = model.pooler.get_pooling_updates(task)
4224
4225
        to_update.apply(dummy_pooling_params)

4226
        dummy_metadata = PoolingMetadata(
4227
4228
4229
4230
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
4231

4232
4233
4234
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
4235

4236
        try:
4237
4238
4239
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
4240
        except RuntimeError as e:
4241
            if "out of memory" in str(e):
4242
                raise RuntimeError(
4243
4244
4245
                    "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 "
4246
4247
                    "initializing the engine."
                ) from e
4248
4249
            else:
                raise e
4250
4251
4252
4253
4254
4255
4256

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
4257
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4259
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
4260
            if self.scheduler_config.enable_chunked_prefill:
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                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks with chunked prefill enabled. "
                    "Please add --no-enable-chunked-prefill to your "
                    "config or CLI args. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )
            else:
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )

4277
        output_size = dict[PoolingTask, float]()
4278
        for task in supported_pooling_tasks:
4279
4280
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
4281
            output_size[task] = sum(o.nbytes for o in output)
4282
4283
4284
4285
            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)
4286

4287
    def profile_run(self) -> None:
4288
        # Profile with multimodal encoder & encoder cache.
4289
        if self.supports_mm_inputs:
4290
4291
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
4292
                logger.info(
4293
                    "Skipping memory profiling for multimodal encoder and "
4294
4295
                    "encoder cache."
                )
4296
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4303
            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.
4304
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
4305
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                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
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4316

                    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,
                    )
4317

4318
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                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
4323

4324
                    # Run multimodal encoder.
4325
                    dummy_encoder_outputs = self.model.embed_multimodal(
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                        **batched_dummy_mm_inputs
                    )
4328

4329
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4332
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
4333

4334
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4336
                    # 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
4337
4338
                    # (max_tokens_for_modality, hidden_size) and scatter
                    # encoder output into it.
4339
                    encoder_output_shape = dummy_encoder_outputs[0].shape
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                    max_mm_tokens_per_item = mm_budget.max_tokens_by_modality[
                        dummy_modality
                    ]
                    if encoder_output_shape[0] < max_mm_tokens_per_item:
                        encoder_hidden_size = encoder_output_shape[-1]
4345
4346
4347
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
4348
                                (max_mm_tokens_per_item, encoder_hidden_size)
4349
                            )
4350
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4355
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

4356
                    # Cache the dummy encoder outputs.
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                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
4358

4359
        # Add `is_profile` here to pre-allocate communication buffers
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        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
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        if get_pp_group().is_last_rank:
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            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
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        else:
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            output = None
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        self._sync_device()
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        del hidden_states, output
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        self.encoder_cache.clear()
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        gc.collect()
4374

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    def capture_model(self) -> int:
4376
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
4377
            logger.warning(
4378
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
4381
            return 0
4382

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4384
        compilation_counter.num_gpu_runner_capture_triggers += 1

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        start_time = time.perf_counter()

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        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
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                    gc.collect()
4402

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        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
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        set_cudagraph_capturing_enabled(True)
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        with freeze_gc(), graph_capture(device=self.device):
4408
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
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            cudagraph_mode = self.compilation_config.cudagraph_mode
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            assert cudagraph_mode is not None
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            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

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            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
4422
                # make sure we capture the largest batch size first
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                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
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                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
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                    uniform_decode=False,
                )
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            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
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            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and cudagraph_mode.separate_routine()
            ):
                max_num_tokens = (
                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
                )
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                decode_cudagraph_batch_sizes = [
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                    x
                    for x in self.cudagraph_batch_sizes
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                    if max_num_tokens >= x >= self.uniform_decode_query_len
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                ]
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                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
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                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
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                    uniform_decode=True,
                )
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            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

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        # Disable cudagraph capturing globally, so any unexpected cudagraph
        # capturing will be detected and raise an error after here.
        # Note: We don't put it into graph_capture context manager because
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        # we may do lazy capturing in future that still allows capturing
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        # after here.
        set_cudagraph_capturing_enabled(False)
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        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
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        logger.info_once(
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            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
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            scope="local",
4474
        )
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        return cuda_graph_size
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    def _capture_cudagraphs(
        self,
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        compilation_cases: list[tuple[int, bool]],
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        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
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        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
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                    cudagraph_runtime_mode.name,
                ),
            )
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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,
                )
4514
            )
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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,
4540
                activate_lora=activate_lora,
Rémi Delacourt's avatar
Rémi Delacourt committed
4541
                is_graph_capturing=True,
4542
            )
4543
        self.maybe_remove_all_loras(self.lora_config)
4544

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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"
4550

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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,
4557
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4558
            layer_type = cast(type[Any], AttentionLayerBase)
4559
            layers = get_layers_from_vllm_config(
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                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
4561
            )
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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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4574

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
4575
                        attn_backend,  # type: ignore[arg-type]
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4577
                    )

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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]
4582
                key = (full_cls_name, layer_kv_cache_spec)
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4584
4585
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4586
                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()),
            )
4591
4592

        def create_attn_groups(
4593
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4594
            kv_cache_group_id: int,
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4596
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4597
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4598
                attn_group = AttentionGroup(
4599
                    attn_backend,
4600
                    layer_names,
4601
                    kv_cache_spec,
4602
                    kv_cache_group_id,
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4604
                )

4605
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4607
                attn_groups.append(attn_group)
            return attn_groups

4608
        attention_backend_maps = []
4609
        attention_backend_list = []
4610
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4611
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4612
            attention_backend_maps.append(attn_backends[0])
4613
            attention_backend_list.append(attn_backends[1])
4614
4615

        # Resolve cudagraph_mode before actually initialize metadata_builders
4616
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        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4619

4620
4621
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4622

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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
4641
        # Calculate reorder batch threshold (if needed)
4642
4643
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
4644
4645
        self.calculate_reorder_batch_threshold()

4646
    def _check_and_update_cudagraph_mode(
4647
4648
4649
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4650
    ) -> None:
4651
        """
4652
        Resolve the cudagraph_mode when there are multiple attention
4653
        groups with potential conflicting CUDA graph support.
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4655
4656
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4657
        min_cg_support = AttentionCGSupport.ALWAYS
4658
        min_cg_backend_name = None
4659

4660
4661
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4663
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4666
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4671
        for attn_backend_set, kv_cache_group in zip(
            attention_backends, kv_cache_groups
        ):
            for attn_backend in attn_backend_set:
                builder_cls = attn_backend.get_builder_cls()

                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
                    min_cg_backend_name = attn_backend.__name__
4672
4673
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
4674
        assert cudagraph_mode is not None
4675
        # check cudagraph for mixed batch is supported
4676
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4678
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4681
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4682
                f"with {min_cg_backend_name} backend (support: "
4683
4684
                f"{min_cg_support})"
            )
4685
4686
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4687
4688
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4689
                    "make sure compilation mode is VLLM_COMPILE"
4690
                )
4691
4692
4693
4694
4695
                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"
4696
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4697
                    CUDAGraphMode.FULL_AND_PIECEWISE
4698
                )
4699
4700
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4701
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4702
                    CUDAGraphMode.FULL_DECODE_ONLY
4703
                )
4704
4705
            logger.warning(msg)

4706
        # check that if we are doing decode full-cudagraphs it is supported
4707
4708
4709
4710
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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 "
4713
                f"with {min_cg_backend_name} backend (support: "
4714
4715
                f"{min_cg_support})"
            )
4716
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4717
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4719
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4721
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4722
                    "attention is compiled piecewise"
4723
4724
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4725
                    CUDAGraphMode.PIECEWISE
4726
                )
4727
            else:
4728
4729
                msg += (
                    "; setting cudagraph_mode=NONE because "
4730
                    "attention is not compiled piecewise"
4731
4732
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4733
                    CUDAGraphMode.NONE
4734
                )
4735
4736
            logger.warning(msg)

4737
4738
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4739
4740
4741
4742
4743
4744
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4746
        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 "
4747
                f"{min_cg_backend_name} (support: {min_cg_support})"
4748
            )
4749
4750
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4751
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4752
                    CUDAGraphMode.PIECEWISE
4753
                )
4754
4755
            else:
                msg += "; setting cudagraph_mode=NONE"
4756
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4757
                    CUDAGraphMode.NONE
4758
                )
4759
4760
4761
4762
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4763
4764
4765
4766
4767
4768
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4769
                f"supported with {min_cg_backend_name} backend ("
4770
4771
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4772
                "and make sure compilation mode is VLLM_COMPILE"
4773
            )
4774

4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
        # we need to adjust the cudagraph sizes to be a multiple of the uniform
        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
        # Will be removed in the near future when we have seperate cudagraph capture
        # sizes for decode and mixed prefill-decode.
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and cudagraph_mode.separate_routine()
            and self.uniform_decode_query_len > 1
        ):
            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
            )
4789
4790
4791
4792
            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
4793

4794
4795
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4796
        self.compilation_config.cudagraph_mode = cudagraph_mode
4797
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4798
            cudagraph_mode, self.uniform_decode_query_len
4799
        )
4800

4801
4802
    def calculate_reorder_batch_threshold(self) -> None:
        """
4803
4804
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4806
        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.
4807
        """
4808
4809
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

4810
        reorder_batch_thresholds: list[int | None] = [
4811
4812
4813
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
4814
4815
4816
4817
4818
        # 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
4819
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
4820

4821
4822
4823
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4824
4825
    ) -> int:
        """
4826
4827
4828
4829
4830
        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.
4831
4832
4833
4834
4835
4836

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

        Returns:
4837
            The selected block size
4838
4839

        Raises:
4840
            ValueError: If no valid block size found
4841
4842
        """

4843
4844
4845
4846
4847
4848
4849
4850
        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
4851
                for supported_size in backend.get_supported_kernel_block_sizes():
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                    if isinstance(supported_size, int):
                        if block_size == supported_size:
                            is_supported = True
                    elif isinstance(supported_size, MultipleOf):
                        if block_size % supported_size.base == 0:
                            is_supported = True
                    else:
                        raise ValueError(f"Unknown supported size: {supported_size}")
                if not is_supported:
                    return False
            return True

        backends = [group.backend for group in attn_groups]

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

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

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

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

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

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

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

4970
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
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        if not self.kv_cache_config.kv_cache_groups:
            return
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        for attn_groups in self.attn_groups:
            yield from attn_groups
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    def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
        """
        Generate kernel_block_sizes that matches each block_size.

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

        Args:
            kv_cache_config: The KV cache configuration.

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

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
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        kernel_block_sizes: list[int],
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    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
5037
        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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Chen Zhang committed
5091
                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)
5120

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        return kv_caches

5123
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
5126
        """
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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
5136
            for layer_name in group.layer_names:
5137
                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 "
5142
                        f"a tensor of shape {kv_cache.shape}"
5143
                    )
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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:]),
                    )
5149

5150
    def initialize_kv_cache_tensors(
5151
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
5152
    ) -> 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.

5160
        Returns:
5161
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        # Try creating KV caches optimized for kv-connector transfers
        cache_dtype = self.cache_config.cache_dtype
        if self.use_uniform_kv_cache(self.attn_groups, cache_dtype):
            kv_caches, cross_layers_kv_cache, attn_backend = (
                self.allocate_uniform_kv_caches(
                    kv_cache_config,
                    self.attn_groups,
                    cache_dtype,
                    self.device,
                    kernel_block_sizes,
                )
            )
            self.cross_layers_kv_cache = cross_layers_kv_cache
            self.cross_layers_attn_backend = attn_backend
        else:
            # Fallback to the general case
            # Initialize the memory buffer for KV cache
            kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)

            # Change the memory buffer to the desired shape
            kv_caches = self._reshape_kv_cache_tensors(
                kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
            )
5188

5189
        # 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.
5226
            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:
5229
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
5232

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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
        """
5240
        kv_cache_config = deepcopy(kv_cache_config)
5241
        self.kv_cache_config = kv_cache_config
5242
        self.may_add_encoder_only_layers_to_kv_cache_config()
5243
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
5244
        self.initialize_attn_backend(kv_cache_config)
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5250
        # 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)
5251
5252
5253
5254

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

5255
        # Reinitialize need to after initialize_attn_backend
5256
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5259
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
5260

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

Robert Shaw's avatar
Robert Shaw committed
5267
        if has_kv_transfer_group():
5268
            kv_transfer_group = get_kv_transfer_group()
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            if self.cross_layers_kv_cache is not None:
                assert self.cross_layers_attn_backend is not None
                kv_transfer_group.register_cross_layers_kv_cache(
                    self.cross_layers_kv_cache, self.cross_layers_attn_backend
                )
            else:
                kv_transfer_group.register_kv_caches(kv_caches)
5276
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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Robert Shaw committed
5277

5278
        if self.dcp_world_size > 1:
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            layer_type = cast(type[Any], AttentionLayerBase)
            layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
5281
            for layer in layers.values():
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                layer_impl = getattr(layer, "impl", None)
                if layer_impl is None:
                    continue
                assert layer_impl.need_to_return_lse_for_decode, (
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5287
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
5288
                    f"{layer_impl.__class__.__name__} "
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5290
                    "does not return the softmax lse for decode."
                )
5291

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5296
    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
5297
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
5298
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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:
5301
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
5302
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5304
                    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:
5310
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5312
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
5313
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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5316
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """
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        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        layer_type = cast(type[Any], AttentionLayerBase)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
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Chen Zhang committed
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
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        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
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        return pinned.tolist()