model_runner.py 45.9 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import functools
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import gc
import time
from copy import deepcopy

import numpy as np
import torch
import torch.nn as nn

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
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from vllm.distributed.parallel_state import (
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    get_dcp_group,
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    get_pp_group,
    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
from vllm.model_executor.model_loader import get_model_loader
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.sequence import IntermediateTensors
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from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
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from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
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from vllm.v1.worker.cp_utils import check_attention_cp_compatibility
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from vllm.v1.worker.gpu.async_utils import AsyncOutput
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from vllm.v1.worker.gpu.attn_utils import (
    build_attn_metadata,
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    build_slot_mappings_by_layer,
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    get_kv_cache_spec,
    init_attn_backend,
    init_kv_cache,
)
from vllm.v1.worker.gpu.block_table import BlockTables
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from vllm.v1.worker.gpu.buffer_utils import async_copy_to_gpu
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from vllm.v1.worker.gpu.cp_utils import prepare_dcp_local_seq_lens
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from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
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from vllm.v1.worker.gpu.dp_utils import (
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    get_cudagraph_and_dp_padding,
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    make_num_tokens_across_dp,
)
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from vllm.v1.worker.gpu.input_batch import (
    InputBatch,
    InputBuffers,
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    combine_sampled_and_draft_tokens,
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    expand_idx_mapping,
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    get_num_sampled_and_rejected,
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    post_update,
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    prepare_pos_seq_lens,
    prepare_prefill_inputs,
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)
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from vllm.v1.worker.gpu.kv_connector import (
    NO_OP_KV_CONNECTOR,
    KVConnector,
    get_kv_connector,
)
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from vllm.v1.worker.gpu.lora_utils import LoraState
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from vllm.v1.worker.gpu.mm.encoder_runner import EncoderRunner
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from vllm.v1.worker.gpu.mm.mrope_utils import MRopeState
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from vllm.v1.worker.gpu.pp_utils import pp_broadcast, pp_receive
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from vllm.v1.worker.gpu.sample.output import SamplerOutput
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from vllm.v1.worker.gpu.sample.prompt_logprob import PromptLogprobsWorker
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from vllm.v1.worker.gpu.sample.sampler import Sampler
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from vllm.v1.worker.gpu.spec_decode import init_speculator
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from vllm.v1.worker.gpu.spec_decode.eagle.eagle3_utils import (
    set_eagle3_aux_hidden_state_layers,
)
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from vllm.v1.worker.gpu.spec_decode.rejection_sample import rejection_sample
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from vllm.v1.worker.gpu.spec_decode.utils import DraftTokensHandler
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from vllm.v1.worker.gpu.states import RequestState
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from vllm.v1.worker.gpu.structured_outputs import StructuredOutputsWorker
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin

logger = init_logger(__name__)


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class GPUModelRunner(LoRAModelRunnerMixin):
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    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
    ):
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.compilation_config = vllm_config.compilation_config
        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

        self.device = device
        self.dtype = self.model_config.dtype
        self.kv_cache_dtype = self.dtype
        if self.cache_config.cache_dtype != "auto":
            # Quantized KV cache.
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                self.cache_config.cache_dtype
            ]
        self.is_pooling_model = False

        self.vocab_size = self.model_config.get_vocab_size()
        self.max_model_len = self.model_config.max_model_len
        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
        self.max_num_reqs = self.scheduler_config.max_num_seqs
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        self.inputs_embeds_size = self.model_config.get_inputs_embeds_size()
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        # Multimodal
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        self.mm_registry = MULTIMODAL_REGISTRY
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
            self.model_config
        )
        if self.supports_mm_inputs:
            self.encoder_runner = EncoderRunner(
                max_num_tokens=self.max_num_tokens,
                hidden_size=self.inputs_embeds_size,
                dtype=self.dtype,
                device=self.device,
            )
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        self.uses_mrope = self.model_config.uses_mrope
        if self.uses_mrope:
            self.mrope_states = MRopeState(
                max_num_reqs=self.max_num_reqs,
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                max_num_tokens=self.max_num_tokens,
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                max_model_len=self.max_model_len,
                device=self.device,
            )

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        self.use_async_scheduling = self.scheduler_config.async_scheduling
        self.output_copy_stream = torch.cuda.Stream(self.device)
        self.output_copy_event = torch.cuda.Event()

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        # Pipeline parallelism.
        self.pp_size = self.parallel_config.pipeline_parallel_size
        self.use_pp = self.pp_size > 1
        if self.use_pp:
            self.is_first_pp_rank = get_pp_group().is_first_rank
            self.is_last_pp_rank = get_pp_group().is_last_rank
        else:
            self.is_first_pp_rank = True
            self.is_last_pp_rank = True

        # Decode context parallelism.
        self.dcp_size = self.parallel_config.decode_context_parallel_size
        self.use_dcp = self.dcp_size > 1
        self.dcp_rank = get_dcp_group().rank_in_group if self.use_dcp else 0
        self.cp_interleave = self.parallel_config.cp_kv_cache_interleave_size

        self.speculator = None
        self.use_aux_hidden_state_outputs = False
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        if self.speculative_config is not None:
            self.do_spec_decode = True
            self.num_speculative_steps = self.speculative_config.num_speculative_tokens
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            if self.is_last_pp_rank:
                self.speculator = init_speculator(self.vllm_config, self.device)

            if self.speculative_config.method == "eagle3":
                # EAGLE3 may require auxiliary hidden states from target model outputs.
                self.use_aux_hidden_state_outputs = True
                if self.pp_size > 1:
                    raise ValueError("EAGLE3 with pipeline parallel is not supported.")
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        else:
            self.do_spec_decode = False
            self.num_speculative_steps = 0
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        # Draft tokens propagation - for spec-dec + struct outputs.
        self.draft_tokens_handler = DraftTokensHandler(self.device)

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        self.req_states = RequestState(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
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            num_speculative_steps=self.num_speculative_steps,
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            vocab_size=self.vocab_size,
            device=self.device,
        )
        self.input_buffers = InputBuffers(
            max_num_reqs=self.max_num_reqs,
            max_num_tokens=self.max_num_tokens,
            device=self.device,
        )
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        self.sampler = Sampler(
            max_num_reqs=self.max_num_reqs,
            vocab_size=self.vocab_size,
            device=self.device,
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            req_states=self.req_states,
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            logprobs_mode=self.model_config.logprobs_mode,
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            num_speculative_tokens=self.num_speculative_steps + 1,
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        )
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        self.prompt_logprobs_worker = PromptLogprobsWorker(self.max_num_reqs)
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        # CUDA graphs.
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        self.cudagraph_manager = CudaGraphManager(
            self.vllm_config, self.uses_mrope, self.device
        )
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        # Structured outputs worker.
        self.structured_outputs_worker = StructuredOutputsWorker(
            max_num_logits=self.max_num_reqs * (self.num_speculative_steps + 1),
            vocab_size=self.vocab_size,
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            device=self.device,
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        )
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        # LoRA-related workers.
        self.lora_state = LoraState(max_num_reqs=self.max_num_reqs)
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        # KV Connector if configured.
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        self.kv_connector: KVConnector = NO_OP_KV_CONNECTOR

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    def update_max_model_len(self, max_model_len: int) -> None:
        self.max_model_len = max_model_len
        self.req_states.max_model_len = max_model_len

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    @staticmethod
    def get_supported_tasks() -> tuple[str]:
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        return ("generate",)

    def load_model(self, *args, **kwargs) -> None:
        time_before_load = time.perf_counter()
        with DeviceMemoryProfiler() as m:
            model_loader = get_model_loader(self.vllm_config.load_config)
            logger.info("Loading model from scratch...")

            self.model = model_loader.load_model(
                vllm_config=self.vllm_config,
                model_config=self.vllm_config.model_config,
            )
            if self.lora_config:
                self.model = self.load_lora_model(
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                    self.model, self.vllm_config, self.device
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                )
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            if self.use_aux_hidden_state_outputs:
                assert self.speculative_config is not None
                set_eagle3_aux_hidden_state_layers(self.model, self.speculative_config)
            if self.speculator is not None:
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                self.speculator.load_model(self.model)
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        time_after_load = time.perf_counter()

        self.model_memory_usage = m.consumed_memory
        logger.info(
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            "Model loading took %s GiB and %.6f seconds",
            format_gib(m.consumed_memory),
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            time_after_load - time_before_load,
        )

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        prepare_communication_buffer_for_model(self.model)
        if self.do_spec_decode:
            speculator_model = getattr(self.speculator, "model", None)
            if speculator_model is not None:
                prepare_communication_buffer_for_model(speculator_model)

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    def get_model(self) -> nn.Module:
        return self.model

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    @functools.cached_property
    def main_stream(self) -> torch.cuda.Stream:
        # Cache the default CUDA stream to avoid lookup overhead.
        return torch.cuda.current_stream(self.device)

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    def get_kv_cache_spec(self):
        return get_kv_cache_spec(self.vllm_config)

    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        kv_cache_config = deepcopy(kv_cache_config)
        self.kv_cache_config = kv_cache_config
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
        ]

        self.block_tables = BlockTables(
            block_sizes=block_sizes,
            max_num_reqs=self.max_num_reqs,
            max_num_batched_tokens=self.max_num_tokens,
            max_model_len=self.max_model_len,
            device=self.device,
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            cp_size=self.dcp_size,
            cp_rank=self.dcp_rank,
            cp_interleave=self.cp_interleave,
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        )

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        self.attn_backends, self.attn_groups = init_attn_backend(
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            self.kv_cache_config, self.vllm_config, self.device
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        )
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        check_attention_cp_compatibility(self.vllm_config)
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        if self.speculator is not None:
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            # HACK(woosuk)
            self.speculator.set_attn(
                self.kv_cache_config,
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                self.attn_groups,
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                self.block_tables,
            )
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        self.kv_caches: list[torch.Tensor] = []
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        kv_caches_dict = init_kv_cache(
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            self.kv_caches,
            self.compilation_config.static_forward_context,
            self.kv_cache_config,
            self.attn_backends,
            self.device,
        )
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        self.kv_connector = get_kv_connector(self.vllm_config, kv_caches_dict)

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    def prepare_dummy_attn_metadata(self, input_batch: InputBatch) -> None:
        block_tables = self.block_tables.get_dummy_block_tables(input_batch.num_reqs)
        slot_mappings = self.block_tables.get_dummy_slot_mappings(
            input_batch.num_tokens
        )
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        slot_mappings_by_layer = build_slot_mappings_by_layer(
            slot_mappings, self.kv_cache_config
        )
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        attn_metadata = build_attn_metadata(
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            attn_groups=self.attn_groups,
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            num_reqs=input_batch.num_reqs,
            num_tokens=input_batch.num_tokens,
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            query_start_loc_gpu=input_batch.query_start_loc,
            query_start_loc_cpu=torch.from_numpy(input_batch.query_start_loc_np),
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            max_query_len=input_batch.num_scheduled_tokens.max().item(),
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            seq_lens=input_batch.seq_lens,
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            max_seq_len=self.max_model_len,
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            block_tables=block_tables,
            slot_mappings=slot_mappings,
            kv_cache_config=self.kv_cache_config,
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            dcp_local_seq_lens=self.input_buffers.dcp_local_seq_lens,
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        )
        input_batch.attn_metadata = attn_metadata
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        input_batch.slot_mappings = slot_mappings_by_layer
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    @torch.inference_mode()
    def _dummy_run(
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        self, num_tokens: int, *args, skip_attn: bool = True, **kwargs
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    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
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        # Create a dummy scheduler output.
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        num_reqs = min(num_tokens, self.max_num_reqs)
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        num_tokens_per_request = [num_tokens // num_reqs] * num_reqs
        num_tokens_per_request[-1] += num_tokens % num_reqs
        assert sum(num_tokens_per_request) == num_tokens
        num_scheduled_tokens = {
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            f"_dummy_req_{i}": n for i, n in enumerate(num_tokens_per_request)
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        }
        dummy_scheduler_output = SchedulerOutput.make_empty()
        dummy_scheduler_output.total_num_scheduled_tokens = num_tokens
        dummy_scheduler_output.num_scheduled_tokens = num_scheduled_tokens

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        # Disable any use of KVConnector for dummy runs.
        self.kv_connector.set_disabled(True)

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        # For non-first PP ranks, create dummy intermediate_tensors.
        intermediate_tensors = None
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        if not self.is_first_pp_rank:
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            intermediate_tensors = self.model.make_empty_intermediate_tensors(
                batch_size=num_tokens,
                dtype=self.model_config.dtype,
                device=self.device,
            )

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        # Execute the model.
        self.execute_model(
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            dummy_scheduler_output,
            intermediate_tensors=intermediate_tensors,
            dummy_run=True,
            skip_attn_for_dummy_run=skip_attn,
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        )
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        self.kv_connector.set_disabled(False)
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        # Non-last PP ranks don't produce output for sampling.
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        if not self.is_last_pp_rank:
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            return None, None

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        assert self.execute_model_state is not None
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        hidden_states, _, input_batch, _ = self.execute_model_state
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        assert hidden_states is not None  # Last PP rank always has hidden_states
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        sample_hidden_states = hidden_states[input_batch.logits_indices]
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        return hidden_states, sample_hidden_states

    @torch.inference_mode()
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    def _dummy_sampler_run(self, hidden_states: torch.Tensor) -> None:
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        num_reqs = hidden_states.shape[0]
        logits = self.model.compute_logits(hidden_states)
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        idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=self.device)
        idx_mapping_np = np.arange(num_reqs, dtype=np.int32)
        pos = torch.zeros(num_reqs, dtype=torch.int64, device=self.device)
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        dummy_input_ids = torch.zeros(num_reqs, dtype=torch.int32, device=self.device)
        expanded_local_pos = torch.zeros(
            num_reqs, dtype=torch.int32, device=self.device
        )
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        # NOTE(woosuk): During the initial memory profiling, the sampler may skip
        # top_k, top_p, and logprobs, using less GPU memory than what is possible
        # during actual execution.
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        self.sampler(
            logits,
            idx_mapping,
            idx_mapping_np,
            idx_mapping_np,
            pos,
            dummy_input_ids,
            expanded_local_pos,
        )
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    @torch.inference_mode()
    def profile_run(self) -> None:
        hidden_states, sample_hidden_states = self._dummy_run(
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            self.max_num_tokens, skip_attn=True
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        )
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        # Only run sampler on last PP rank (non-last ranks return None).
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        if self.is_last_pp_rank:
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            assert sample_hidden_states is not None
            self._dummy_sampler_run(sample_hidden_states)
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            if self.speculator is not None:
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                num_tokens_across_dp = make_num_tokens_across_dp(
                    self.parallel_config.data_parallel_size, self.max_num_tokens
                )
                self.speculator.run_model(
                    self.max_num_tokens,
                    attn_metadata=None,
                    slot_mappings=None,
                    num_tokens_across_dp=num_tokens_across_dp,
                )

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        torch.cuda.synchronize()
        del hidden_states, sample_hidden_states
        gc.collect()

    def reset_mm_cache(self) -> None:
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        if self.supports_mm_inputs:
            self.encoder_runner.reset_mm_cache()
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    def reset_encoder_cache(self) -> None:
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        if self.supports_mm_inputs:
            self.encoder_runner.reset_encoder_cache()
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    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
        # SP is not supported yet.
        return num_scheduled_tokens

    @torch.inference_mode()
    def capture_model(self) -> int:
        if not self.cudagraph_manager.needs_capture():
            logger.warning(
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
            return 0

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        # TODO (zhanqiu): support CUDA graph for PP.
        if self.use_pp:
            logger.warning_once(
                "Skipping CUDA graph capture because pipeline parallel is "
                "enabled. Pipeline parallel is currently eager-only.",
            )
            return 0

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        start_time = time.perf_counter()
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        gc.collect()
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        torch.cuda.empty_cache()
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        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

        with self.maybe_setup_dummy_loras(self.lora_config):
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            mrope_positions = None
            if self.uses_mrope:
                mrope_positions = self.mrope_states.mrope_positions
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            inputs_embeds = None
            if self.supports_mm_inputs:
                inputs_embeds = self.encoder_runner.inputs_embeds
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            self.cudagraph_manager.capture(
                model=self.model,
                input_buffers=self.input_buffers,
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                mrope_positions=mrope_positions,
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                inputs_embeds=inputs_embeds,
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                block_tables=self.block_tables,
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                attn_groups=self.attn_groups,
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                kv_cache_config=self.kv_cache_config,
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                has_lora=self.lora_config is not None,
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            )
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            if self.speculator is not None:
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                self.speculator.capture_model()
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        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
        return cuda_graph_size

    def warmup_for_prefill(self) -> None:
        # For FlashInfer, we would like to execute a dummy prefill run
        # to trigger JIT compilation.
        if all("FLASHINFER" in b.get_name() for b in self.attn_backends.values()):
            self._dummy_run(self.max_num_tokens, skip_attn=False)
            torch.cuda.synchronize()

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    def finish_requests(self, scheduler_output: SchedulerOutput) -> None:
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        finished_req_ids = scheduler_output.finished_req_ids
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        preempted_req_ids = scheduler_output.preempted_req_ids
        if preempted_req_ids:
            finished_req_ids = finished_req_ids.union(preempted_req_ids)
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        for req_id in finished_req_ids:
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            self.req_states.remove_request(req_id)
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            if self.supports_mm_inputs:
                self.encoder_runner.remove_request(req_id)
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            self.prompt_logprobs_worker.remove_request(req_id)
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            self.lora_state.remove_request(req_id)
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    def free_states(self, scheduler_output: SchedulerOutput) -> None:
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        if self.supports_mm_inputs:
            for mm_hash in scheduler_output.free_encoder_mm_hashes:
                self.encoder_runner.free_encoder_cache(mm_hash)
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    def add_requests(self, scheduler_output: SchedulerOutput) -> None:
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        for new_req_data in scheduler_output.scheduled_new_reqs:
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            assert new_req_data.prompt_token_ids is not None
            assert new_req_data.prefill_token_ids is not None
            assert new_req_data.sampling_params is not None
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            req_id = new_req_data.req_id
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            prompt_len = len(new_req_data.prompt_token_ids)
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            self.req_states.add_request(
                req_id=req_id,
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                prompt_len=prompt_len,
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                all_token_ids=new_req_data.prefill_token_ids,
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                num_computed_tokens=new_req_data.num_computed_tokens,
            )
            req_index = self.req_states.req_id_to_index[req_id]
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            if self.supports_mm_inputs:
                self.encoder_runner.add_request(req_id, new_req_data.mm_features)

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            # Pre-compute M-RoPE positions for prefill.
            if self.uses_mrope:
                self.mrope_states.init_prefill_mrope_positions(
                    req_index,
                    self.model,  # type: ignore
                    new_req_data.prefill_token_ids,
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                    mm_features=new_req_data.mm_features,
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                )

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            self.block_tables.append_block_ids(
                req_index, new_req_data.block_ids, overwrite=True
            )
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            self.sampler.add_request(
                req_index, prompt_len, new_req_data.sampling_params
            )
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            self.prompt_logprobs_worker.add_request(
                req_id, req_index, new_req_data.sampling_params
            )
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            self.lora_state.add_request(req_id, req_index, new_req_data.lora_request)
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        if scheduler_output.scheduled_new_reqs:
            self.req_states.apply_staged_writes()
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            self.sampler.apply_staged_writes()
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            if self.uses_mrope:
                self.mrope_states.apply_staged_writes()

    def update_requests(self, scheduler_output: SchedulerOutput) -> None:
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        # Add new blocks for the existing requests.
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        reqs = scheduler_output.scheduled_cached_reqs
        for req_new_block_ids, req_id in zip(reqs.new_block_ids, reqs.req_ids):
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            if req_new_block_ids is not None:
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                req_index = self.req_states.req_id_to_index[req_id]
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                self.block_tables.append_block_ids(
                    req_index, req_new_block_ids, overwrite=False
                )
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    def prepare_inputs(
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        self, scheduler_output: SchedulerOutput, num_tokens_after_padding: int
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    ) -> InputBatch:
        num_tokens = scheduler_output.total_num_scheduled_tokens
        assert num_tokens > 0
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        num_tokens_per_req = scheduler_output.num_scheduled_tokens
        num_reqs = len(num_tokens_per_req)
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        # Decode first, then prefill.
        # batch_idx -> req_id
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        req_ids = sorted(num_tokens_per_req, key=num_tokens_per_req.get)  # type: ignore[arg-type]
        numtoks_iter = map(num_tokens_per_req.get, req_ids)
        num_scheduled_tokens = np.fromiter(numtoks_iter, dtype=np.int32, count=num_reqs)
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        idx_mapping_iter = map(self.req_states.req_id_to_index.get, req_ids)
        idx_mapping_np = np.fromiter(idx_mapping_iter, dtype=np.int32, count=num_reqs)
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        idx_mapping = async_copy_to_gpu(idx_mapping_np, device=self.device)
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        # Get the number of draft tokens for each request.
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        draft_tokens = scheduler_output.scheduled_spec_decode_tokens
        if not draft_tokens:
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            # No draft token scheduled (common case).
            total_num_draft_tokens = 0
            total_num_logits = num_reqs
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            cu_num_logits_np = np.arange(num_reqs + 1, dtype=np.int32)
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            cu_num_logits = torch.arange(
                num_reqs + 1, device=self.device, dtype=torch.int32
            )
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            expanded_idx_mapping = idx_mapping
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            expanded_local_pos = torch.zeros(
                num_reqs, dtype=torch.int32, device=self.device
            )
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        else:
            num_draft_tokens = np.array(
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                [len(draft_tokens.get(req_id, ())) for req_id in req_ids],
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                dtype=np.int32,
            )
            total_num_draft_tokens = int(num_draft_tokens.sum())
            total_num_logits = num_reqs + total_num_draft_tokens

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            num_logits = num_draft_tokens + 1
            cu_num_logits_np = np.empty(num_reqs + 1, dtype=np.int32)
            cu_num_logits_np[0] = 0
            np.cumsum(num_logits, out=cu_num_logits_np[1:])
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            cu_num_logits = async_copy_to_gpu(cu_num_logits_np, device=self.device)
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            max_expand_len = self.num_speculative_steps + 1
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            expanded_idx_mapping, expanded_local_pos = expand_idx_mapping(
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                idx_mapping, total_num_logits, cu_num_logits, max_expand_len
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            )

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        # Block tables: num_kv_cache_groups x [num_reqs, max_num_blocks]
        block_tables = self.block_tables.gather_block_tables(idx_mapping)

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        # Get query_start_loc.
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        query_start_loc_np = np.empty(self.max_num_reqs + 1, dtype=np.int32)
        query_start_loc_np[0] = 0
        np.cumsum(num_scheduled_tokens, out=query_start_loc_np[1 : num_reqs + 1])
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        # Pad for full CUDA graph mode.
        # Some attention backends like FA3 require query_start_loc to be non-decreasing.
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        query_start_loc_np[num_reqs + 1 :] = num_tokens
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        async_copy_to_gpu(query_start_loc_np, out=self.input_buffers.query_start_loc)

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        query_start_loc_np = query_start_loc_np[: num_reqs + 1]
        query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
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        max_query_len = num_scheduled_tokens.max().item()
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        # Get prefill tokens if any.
        if self.req_states.any_prefills(idx_mapping_np):
            prepare_prefill_inputs(
                self.input_buffers.input_ids,
                self.req_states.next_prefill_tokens,
                idx_mapping,
                query_start_loc,
                self.req_states.all_token_ids.gpu,
                self.req_states.prefill_len.gpu,
                self.req_states.num_computed_tokens.gpu,
            )
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        # Prepare positions and seq_lens.
        prepare_pos_seq_lens(
            idx_mapping,
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            query_start_loc,
            self.req_states.num_computed_tokens.gpu,
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            self.input_buffers.positions,
            self.input_buffers.seq_lens,
        )
        seq_lens = self.input_buffers.seq_lens[:num_reqs]

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        if self.use_dcp:
            # Prepare dcp local seq_lens.
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            prepare_dcp_local_seq_lens(
                self.input_buffers.dcp_local_seq_lens,
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                self.input_buffers.seq_lens,
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                num_reqs,
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                self.dcp_size,
                self.dcp_rank,
                self.cp_interleave,
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            )
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        dcp_local_seq_lens = self.input_buffers.dcp_local_seq_lens[:num_reqs]
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        # Prepare M-RoPE positions.
        if self.uses_mrope:
            self.mrope_states.prepare_mrope_positions(
                idx_mapping,
                query_start_loc,
                self.req_states.prefill_len.gpu,
                self.req_states.num_computed_tokens.gpu,
            )

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        # Some input token ids are directly read from the last sampled tokens
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        # and draft tokens. Also, get the logits indices to sample tokens from.
        logits_indices = combine_sampled_and_draft_tokens(
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            self.input_buffers.input_ids,
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            idx_mapping,
            self.req_states.last_sampled_tokens,
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            query_start_loc,
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            seq_lens,
            self.req_states.prefill_len.gpu,
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            self.req_states.draft_tokens,
            cu_num_logits,
            total_num_logits,
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        )

        # Compute slot mappings: [num_kv_cache_groups, num_tokens]
        slot_mappings = self.block_tables.compute_slot_mappings(
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            idx_mapping,
            query_start_loc,
            self.input_buffers.positions[:num_tokens],
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        )
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        # Layer name -> slot mapping.
        slot_mappings_by_layer = build_slot_mappings_by_layer(
            slot_mappings, self.kv_cache_config
        )
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        # Layer name -> attention metadata.
        attn_metadata = build_attn_metadata(
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            attn_groups=self.attn_groups,
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            num_reqs=num_reqs,
            num_tokens=num_tokens,
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            query_start_loc_gpu=query_start_loc,
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            query_start_loc_cpu=query_start_loc_cpu,
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            max_query_len=max_query_len,
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            seq_lens=self.input_buffers.seq_lens,
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            max_seq_len=self.max_model_len,
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            block_tables=block_tables,
            slot_mappings=slot_mappings,
            kv_cache_config=self.kv_cache_config,
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            dcp_local_seq_lens=dcp_local_seq_lens,
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        )

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        input_ids = self.input_buffers.input_ids[:num_tokens_after_padding]
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        positions = self.input_buffers.positions[:num_tokens_after_padding]
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        mrope_positions = None
        if self.uses_mrope:
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            mrope_positions = self.mrope_states.mrope_positions
            mrope_positions = mrope_positions[:, :num_tokens_after_padding]
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        return InputBatch(
            req_ids=req_ids,
            num_reqs=num_reqs,
            idx_mapping=idx_mapping,
            idx_mapping_np=idx_mapping_np,
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            expanded_idx_mapping=expanded_idx_mapping,
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            expanded_local_pos=expanded_local_pos,
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            num_scheduled_tokens=num_scheduled_tokens,
            num_tokens=num_tokens,
            num_tokens_after_padding=num_tokens_after_padding,
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            num_draft_tokens=total_num_draft_tokens,
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            query_start_loc=query_start_loc,
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            query_start_loc_np=query_start_loc_np,
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            seq_lens=seq_lens,
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            input_ids=input_ids,
            positions=positions,
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            mrope_positions=mrope_positions,
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            inputs_embeds=None,
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            attn_metadata=attn_metadata,
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            slot_mappings=slot_mappings_by_layer,
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            logits_indices=logits_indices,
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            cu_num_logits=cu_num_logits,
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            cu_num_logits_np=cu_num_logits_np,
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            has_structured_output_reqs=scheduler_output.has_structured_output_requests,
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        )

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    @torch.inference_mode()
    def get_mm_embeddings(
        self,
        scheduled_encoder_inputs: dict[str, list[int]],
        input_batch: InputBatch,
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        mm_hashes, mm_kwargs = self.encoder_runner.prepare_mm_inputs(
            scheduled_encoder_inputs
        )
        self.encoder_runner.execute_mm_encoder(self.model, mm_hashes, mm_kwargs)
        mm_embeds, is_mm_embed = self.encoder_runner.gather_mm_embeddings(
            input_batch.req_ids,
            input_batch.num_tokens,
            input_batch.num_scheduled_tokens,
            input_batch.query_start_loc_np,
            self.req_states.prefill_len.np[input_batch.idx_mapping_np],
            self.req_states.num_computed_prefill_tokens[input_batch.idx_mapping_np],
        )
        return mm_embeds, is_mm_embed

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    def sample(
        self,
        hidden_states: torch.Tensor,
        input_batch: InputBatch,
        grammar_output: GrammarOutput | None,
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    ) -> tuple[SamplerOutput, torch.Tensor, torch.Tensor]:
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        sample_hidden_states = hidden_states[input_batch.logits_indices]
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        sample_pos = input_batch.positions[input_batch.logits_indices]
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        input_ids = input_batch.input_ids[input_batch.logits_indices]
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        logits = self.model.compute_logits(sample_hidden_states)
        if grammar_output is not None:
            # Apply grammar bitmask to the logits in-place.
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            self.structured_outputs_worker.apply_grammar_bitmask(
                logits,
                input_batch,
                grammar_output.structured_output_request_ids,
                grammar_output.grammar_bitmask,
            )
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        # Sample tokens and compute logprobs (if needed).
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        sampler_output = self.sampler(
            logits,
            input_batch.expanded_idx_mapping,
            input_batch.idx_mapping_np,
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            input_batch.cu_num_logits_np,
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            sample_pos,
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            input_ids,
            input_batch.expanded_local_pos,
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        )
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        if input_batch.num_draft_tokens == 0:
            # No draft tokens (common case).
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            num_sampled = torch.ones(
                input_batch.num_reqs, dtype=torch.int32, device=self.device
            )
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        else:
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            # Rejection sampling for spec decoding.
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            sampled_tokens, num_sampled = rejection_sample(
                sampler_output.sampled_token_ids,
                input_ids,
                input_batch.cu_num_logits,
                self.num_speculative_steps,
            )
            sampler_output.sampled_token_ids = sampled_tokens
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        # Get the number of sampled and rejected tokens.
        # For chunked prefills, num_sampled and num_rejected are both 0.
        num_sampled, num_rejected = get_num_sampled_and_rejected(
            num_sampled,
            input_batch.seq_lens,
            input_batch.cu_num_logits,
            input_batch.idx_mapping,
            self.req_states.prefill_len.gpu,
        )
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        return sampler_output, num_sampled, num_rejected
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    def postprocess(
        self,
        input_batch: InputBatch,
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        sampled_tokens: torch.Tensor,
        num_sampled: torch.Tensor,
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        num_rejected: torch.Tensor,
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    ) -> None:
        # Update the number of computed tokens.
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        post_update(
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            input_batch.idx_mapping,
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            self.req_states.num_computed_tokens.gpu,
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            self.req_states.last_sampled_tokens,
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            self.sampler.penalties_state.output_bin_counts,
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            sampled_tokens,
            num_sampled,
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            num_rejected,
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            input_batch.query_start_loc,
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            self.req_states.all_token_ids.gpu,
            self.req_states.total_len.gpu,
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        )
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        # Update the number of computed prefill tokens.
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        idx_mapping_np = input_batch.idx_mapping_np
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        computed_prefill = self.req_states.num_computed_prefill_tokens
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        computed_prefill[idx_mapping_np] += input_batch.num_scheduled_tokens
        np.minimum(
            computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
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        )

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    @torch.inference_mode()
    def propose_draft(
        self,
        input_batch: InputBatch,
        last_hidden_states: torch.Tensor,
        aux_hidden_states: list[torch.Tensor] | None,
        num_sampled: torch.Tensor,
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        num_rejected: torch.Tensor,
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    ) -> torch.Tensor:
        assert self.speculator is not None
        draft_tokens = self.speculator.propose(
            input_batch,
            last_hidden_states,
            aux_hidden_states,
            num_sampled,
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            num_rejected,
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            self.req_states.last_sampled_tokens,
            self.req_states.next_prefill_tokens,
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            self.sampler.sampling_states.temperature.gpu,
            self.sampler.sampling_states.seeds.gpu,
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        )
        return draft_tokens

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    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: SchedulerOutput,
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        intermediate_tensors: IntermediateTensors | None = None,
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        dummy_run: bool = False,
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        skip_attn_for_dummy_run: bool = False,
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    ) -> ModelRunnerOutput | IntermediateTensors | None:
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        if not dummy_run:
            # Update the request states.
            self.finish_requests(scheduler_output)
            self.free_states(scheduler_output)
            self.add_requests(scheduler_output)
            self.update_requests(scheduler_output)
            self.block_tables.apply_staged_writes()
            if scheduler_output.total_num_scheduled_tokens == 0:
                # No need to run the model.
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                empty_output = self.kv_connector.no_forward(scheduler_output)
                return empty_output
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        # Get local cudagraph mode and size.
        local_cudagraph_mode, local_cudagraph_size = (
            self.cudagraph_manager.get_cudagraph_runtime_mode(
                num_reqs=len(scheduler_output.num_scheduled_tokens),
                num_tokens=scheduler_output.total_num_scheduled_tokens,
                max_query_len=max(scheduler_output.num_scheduled_tokens.values()),
            )
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        )
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        # DP sync: num_tokens + cudagraph_size + cudagraph_mode
        num_tokens_after_padding, num_tokens_across_dp, synced_cudagraph_mode = (
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            get_cudagraph_and_dp_padding(
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                scheduler_output.total_num_scheduled_tokens,
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                local_cudagraph_size,
                local_cudagraph_mode.value,
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                self.parallel_config.data_parallel_size,
                self.parallel_config.data_parallel_rank,
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            )
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        )
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        cudagraph_runtime_mode = CUDAGraphMode(synced_cudagraph_mode)
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        if num_tokens_after_padding == 0:
            # All DP ranks have zero tokens to run.
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            empty_output = self.kv_connector.no_forward(scheduler_output)
            return empty_output
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        if not dummy_run:
            # Common case.
            # Prepare all the inputs and copy to the input buffers.
            input_batch = self.prepare_inputs(
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                scheduler_output, num_tokens_after_padding
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            )
            if self.lora_config:
                # Activate LoRA adapters.
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                lora_inputs = self.lora_state.make_lora_inputs(
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                    input_batch.req_ids,
                    input_batch.idx_mapping_np,
                    input_batch.num_scheduled_tokens,
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                )
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                self._set_active_loras(*lora_inputs)
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            # Only first PP rank prepares multimodal embeddings.
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            if self.supports_mm_inputs and self.is_first_pp_rank:
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                mm_embeds, is_mm_embed = self.get_mm_embeddings(
                    scheduler_output.scheduled_encoder_inputs, input_batch
                )
                inputs_embeds = self.encoder_runner.get_inputs_embeds(
                    self.model, input_batch.input_ids, mm_embeds, is_mm_embed
                )
                input_batch.inputs_embeds = inputs_embeds[
                    : input_batch.num_tokens_after_padding
                ]
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        else:
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            # No actual tokens to run. A dummy run for DP or memory profiling.
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            num_reqs = min(num_tokens_after_padding, self.max_num_reqs)
            input_batch = InputBatch.make_dummy(
                num_reqs=num_reqs,
                num_tokens=num_tokens_after_padding,
                input_buffers=self.input_buffers,
                device=self.device,
            )
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            if self.uses_mrope:
                input_batch.mrope_positions = self.mrope_states.mrope_positions[
                    :, :num_tokens_after_padding
                ]
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            if not skip_attn_for_dummy_run:
                self.prepare_dummy_attn_metadata(input_batch)
            # FIXME(woosuk): Fix warmup for LoRA.
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        # Run model.
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        if cudagraph_runtime_mode == CUDAGraphMode.FULL:
            # Use explicit cudagraph replay for FULL mode.
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            # NOTE(woosuk): Here, we don't need to pass the input tensors,
            # because they are already copied to the CUDA graph input buffers.
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            self.kv_connector.pre_forward(scheduler_output)
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            model_output = self.cudagraph_manager.run_fullgraph(
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                input_batch.num_tokens_after_padding
            )
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            if self.use_aux_hidden_state_outputs:
                hidden_states, aux_hidden_states = model_output
            else:
                hidden_states = model_output
                aux_hidden_states = None
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        else:
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            # For piecewise and eager mode, just call model().
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            positions = input_batch.positions
            if self.uses_mrope:
                assert input_batch.mrope_positions is not None
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                positions = input_batch.mrope_positions
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            if self.is_first_pp_rank:
                input_ids = input_batch.input_ids
                inputs_embeds = input_batch.inputs_embeds
                assert intermediate_tensors is None
            else:
                input_ids = None
                inputs_embeds = None
                assert intermediate_tensors is not None

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            batch_descriptor = BatchDescriptor(
                num_tokens=input_batch.num_tokens_after_padding,
                has_lora=self.lora_config is not None,
            )

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            with set_forward_context(
                input_batch.attn_metadata,
                self.vllm_config,
                num_tokens=input_batch.num_tokens_after_padding,
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                cudagraph_runtime_mode=cudagraph_runtime_mode,
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                num_tokens_across_dp=num_tokens_across_dp,
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                batch_descriptor=batch_descriptor,
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                slot_mapping=input_batch.slot_mappings,
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            ):
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                self.kv_connector.pre_forward(scheduler_output)
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                model_output = self.model(
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                    input_ids=input_ids,
                    positions=positions,
                    inputs_embeds=inputs_embeds,
                    intermediate_tensors=intermediate_tensors,
                )
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                if self.use_aux_hidden_state_outputs:
                    hidden_states, aux_hidden_states = model_output
                else:
                    hidden_states = model_output
                    aux_hidden_states = None
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        kv_connector_output = self.kv_connector.post_forward(scheduler_output)
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        if not self.is_last_pp_rank:
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            # Non-last PP rank: return IntermediateTensors for sending.
            assert isinstance(hidden_states, IntermediateTensors)
            hidden_states.kv_connector_output = kv_connector_output
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            self.execute_model_state = (None, None, input_batch, kv_connector_output)
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            return hidden_states

        # Last rank (or no PP): hidden_states is a tensor for sampling.
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        assert isinstance(hidden_states, torch.Tensor)
        self.execute_model_state = (
            hidden_states,
            aux_hidden_states,
            input_batch,
            kv_connector_output,
        )
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        return None

    @torch.inference_mode()
    def sample_tokens(
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        self, grammar_output: GrammarOutput | None
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    ) -> AsyncOutput | ModelRunnerOutput | None:
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        assert self.execute_model_state is not None
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        hidden_states, aux_hidden_states, input_batch, kv_connector_output = (
            self.execute_model_state
        )
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        self.execute_model_state = None  # type: ignore

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        if not self.is_last_pp_rank:
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            # Non-last PP rank: hidden_states is None because this rank produced
            # IntermediateTensors instead of final hidden states. Receive the
            # sampled tokens broadcast from the last rank and update local state.
            sampled, num_sampled, num_rejected = pp_receive(
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                input_batch.num_reqs, max_sample_len=self.num_speculative_steps + 1
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            )
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            self.postprocess(input_batch, sampled, num_sampled, num_rejected)
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            return None

        # Last rank: sample tokens
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        sampler_output, num_sampled, num_rejected = self.sample(
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            hidden_states, input_batch, grammar_output
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        )
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        if self.use_pp:
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            # Broadcast to non-last PP ranks (handles spec decode multi-token).
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            pp_broadcast(sampler_output.sampled_token_ids, num_sampled, num_rejected)
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        prompt_logprobs_dict = self.prompt_logprobs_worker.compute_prompt_logprobs(
            self.model.compute_logits,
            hidden_states,
            input_batch,
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            self.req_states.all_token_ids.gpu,
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            self.req_states.num_computed_tokens.gpu,
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            self.req_states.prompt_len.np,
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            self.req_states.prefill_len.np,
            self.req_states.num_computed_prefill_tokens,
        )
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        # Prepare the model runner output.
        model_runner_output = ModelRunnerOutput(
            req_ids=input_batch.req_ids,
            # NOTE(woosuk): req_id_to_index is unused in this model runner.
            # Only for compatibility with the existing model runner and scheduler.
            req_id_to_index={req_id: i for i, req_id in enumerate(input_batch.req_ids)},
            sampled_token_ids=None,  # type: ignore
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            prompt_logprobs_dict=prompt_logprobs_dict,  # type: ignore[arg-type]
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            kv_connector_output=kv_connector_output,
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        )
        async_output = AsyncOutput(
            model_runner_output=model_runner_output,
            sampler_output=sampler_output,
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            num_sampled_tokens=num_sampled,
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            main_stream=self.main_stream,
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            copy_stream=self.output_copy_stream,
            copy_event=self.output_copy_event,
        )

        # Postprocess results and update request states.
        # NOTE: This is intentionally done after creating the AsyncOutput,
        # ensuring that `copy_event` is recorded before calling postprocess.
        # This sequencing may slightly reduce latency as async D2H copy does not
        # need to wait for the postprocess to finish.
        self.postprocess(
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            input_batch, sampler_output.sampled_token_ids, num_sampled, num_rejected
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        )
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        if self.speculator is not None:
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            draft_tokens = self.propose_draft(
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                input_batch,
                hidden_states,
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                aux_hidden_states,
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                num_sampled,
                num_rejected,
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            )
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            self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
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            self.draft_tokens_handler.set_draft_tokens(input_batch, draft_tokens)
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        if self.use_async_scheduling:
            return async_output
        return async_output.get_output()
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    def take_draft_token_ids(self) -> DraftTokenIds | None:
        return self.draft_tokens_handler.get_draft_tokens()