eagle.py 47.7 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 ast
from dataclasses import replace
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from importlib.util import find_spec
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from typing import Optional
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import numpy as np
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import torch
import torch.nn as nn

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from vllm.config import CompilationLevel, VllmConfig, get_layers_from_vllm_config
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.forward_context import 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.model_loader import get_model
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from vllm.model_executor.models import supports_multimodal
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from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.platforms import current_platform
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from vllm.utils import is_pin_memory_available
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.attention.backends.tree_attn import (
    TreeAttentionMetadata,
    TreeAttentionMetadataBuilder,
)
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from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
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from vllm.v1.attention.backends.utils import (
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
)
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm.v1.utils import CpuGpuBuffer
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.ubatching import dbo_current_ubatch_id
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logger = init_logger(__name__)

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PADDING_SLOT_ID = -1

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class EagleProposer:
    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
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        runner=None,
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    ):
        self.vllm_config = vllm_config
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        self.speculative_config = vllm_config.speculative_config
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        assert self.speculative_config is not None
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        self.draft_model_config = self.speculative_config.draft_model_config
        self.method = self.speculative_config.method
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        self.runner = runner
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        self.device = device
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        self.dtype = vllm_config.model_config.dtype
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        self.max_model_len = vllm_config.model_config.max_model_len
        self.block_size = vllm_config.cache_config.block_size
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        self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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        self.token_arange_np = np.arange(self.max_num_tokens)
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        # We need to get the hidden size from the draft model config because
        # the draft model's hidden size can be different from the target model's
        # hidden size (e.g., Llama 3.3 70B).
        self.hidden_size = self.draft_model_config.get_hidden_size()
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        # Multi-modal data support
        self.mm_registry = MULTIMODAL_REGISTRY
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            vllm_config.model_config
        )
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        self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None
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        self.draft_indexer_metadata_builder: Optional[AttentionMetadataBuilder] = None
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        self.attn_layer_names: list[str] = []
        self.indexer_layer_names: list[str] = []
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        self.use_cuda_graph = (
            not current_platform.is_xpu()
            and self.vllm_config.compilation_config.level == CompilationLevel.PIECEWISE
            and not self.vllm_config.model_config.enforce_eager
            and not self.speculative_config.enforce_eager
        )
        self.cudagraph_batch_sizes = (
            list(reversed(self.vllm_config.compilation_config.cudagraph_capture_sizes))
            if self.use_cuda_graph
            else []
        )
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        # persistent buffers for cuda graph
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        self.input_ids = torch.zeros(
            self.max_num_tokens, dtype=torch.int32, device=device
        )
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        self.uses_mrope = self.vllm_config.model_config.uses_mrope
        if self.uses_mrope:
            # M-RoPE need (3, max_num_tokens)
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            self.mrope_positions = torch.zeros(
                (3, self.max_num_tokens), dtype=torch.int64, device=device
            )
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        else:
            # RoPE need (max_num_tokens,)
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            self.positions = torch.zeros(
                self.max_num_tokens, dtype=torch.int64, device=device
            )
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        self.hidden_states = torch.zeros(
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            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
        )
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        # We need +1 here because the arange is used to set query_start_loc,
        # which has one more element than batch_size.
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        max_batch_size = vllm_config.scheduler_config.max_num_seqs
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        max_num_slots_for_arange = max(max_batch_size + 1, self.max_num_tokens)
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        self.arange = torch.arange(
            max_num_slots_for_arange, device=device, dtype=torch.int32
        )
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        self.inputs_embeds = torch.zeros(
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            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
        )
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        self.backup_next_token_ids = CpuGpuBuffer(
            max_batch_size,
            dtype=torch.int32,
            pin_memory=is_pin_memory_available(),
            device=device,
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            with_numpy=True,
        )
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        # Determine allowed attention backends once during initialization.
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        self.allowed_attn_types: Optional[tuple] = None
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        if current_platform.is_rocm():
            rocm_types = [TritonAttentionMetadata, FlashAttentionMetadata]
            # vllm.v1.attention.backends.rocm_aiter_fa is an optional backend
            if find_spec("vllm.v1.attention.backends.rocm_aiter_fa"):
                from vllm.v1.attention.backends.rocm_aiter_fa import (
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                    AiterFlashAttentionMetadata,
                )

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                rocm_types.append(AiterFlashAttentionMetadata)
            self.allowed_attn_types = tuple(rocm_types)

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        # Parse the speculative token tree.
        spec_token_tree = self.speculative_config.speculative_token_tree
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        self.tree_choices: list[tuple[int, ...]] = ast.literal_eval(spec_token_tree)
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        tree_depth = len(self.tree_choices[-1])
        # Precompute per-level properties of the tree.
        num_drafts_per_level = [0] * tree_depth
        for node in self.tree_choices:
            num_drafts_per_level[len(node) - 1] += 1
        self.cu_drafts_per_level = [num_drafts_per_level[0]]
        self.child_drafts_per_level = [num_drafts_per_level[0]]
        for level in range(1, tree_depth):
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            self.cu_drafts_per_level.append(
                self.cu_drafts_per_level[-1] + num_drafts_per_level[level]
            )
            self.child_drafts_per_level.append(
                num_drafts_per_level[level] // num_drafts_per_level[level - 1]
            )
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        # Precompute draft position offsets in flattened tree.
        self.tree_draft_pos_offsets = torch.arange(
            1,
            len(self.tree_choices) + 1,
            device=device,
            dtype=torch.int32,
        ).repeat(max_batch_size, 1)

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

    def _set_positions(self, num_tokens: int, positions: torch.Tensor):
        if self.uses_mrope:
            self.mrope_positions[:, :num_tokens] = positions
        else:
            self.positions[:num_tokens] = positions

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    def propose(
        self,
        # [num_tokens]
        target_token_ids: torch.Tensor,
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        # [num_tokens] or [3, num_tokens] when M-RoPE is enabled
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        target_positions: torch.Tensor,
        # [num_tokens, hidden_size]
        target_hidden_states: torch.Tensor,
        # [batch_size]
        next_token_ids: torch.Tensor,
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        last_token_indices: Optional[torch.Tensor],
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        common_attn_metadata: CommonAttentionMetadata,
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        sampling_metadata: SamplingMetadata,
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        mm_embed_inputs: Optional[tuple[list[torch.Tensor], torch.Tensor]] = None,
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    ) -> torch.Tensor:
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        num_tokens = target_token_ids.shape[0]
        batch_size = next_token_ids.shape[0]
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        if last_token_indices is None:
            last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
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        if self.method == "eagle3":
            assert isinstance(self.model, Eagle3LlamaForCausalLM)
            target_hidden_states = self.model.combine_hidden_states(
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                target_hidden_states
            )
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            assert target_hidden_states.shape[-1] == self.hidden_size
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        # Shift the input ids by one token.
        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
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        self.input_ids[: num_tokens - 1] = target_token_ids[1:]
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        # Replace the last token with the next token.
        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
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        self.input_ids[last_token_indices] = next_token_ids
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        assert self.runner is not None
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        # FIXME: need to consider multiple kv_cache_groups
        ubatch_id = dbo_current_ubatch_id()
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        attn_metadata_builder = self.runner.attn_groups[0][0].metadata_builders[
            ubatch_id
        ]
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        attn_metadata = attn_metadata_builder.build_for_drafting(
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            common_attn_metadata=common_attn_metadata, draft_index=0
        )
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        # FIXME: support hybrid kv for draft model (remove separate indexer)
        if self.draft_indexer_metadata_builder:
            draft_indexer_metadata = (
                self.draft_indexer_metadata_builder.build_for_drafting(
                    common_attn_metadata=common_attn_metadata,
                    draft_index=0,
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                )
            )
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        else:
            draft_indexer_metadata = None
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        # At this moment, we assume all eagle layers belong to the same KV
        # cache group, thus using the same attention metadata.
        per_layer_attn_metadata = {}
        for layer_name in self.attn_layer_names:
            per_layer_attn_metadata[layer_name] = attn_metadata
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        for layer_name in self.indexer_layer_names:
            assert draft_indexer_metadata is not None
            per_layer_attn_metadata[layer_name] = draft_indexer_metadata

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        if self.use_cuda_graph and num_tokens <= self.cudagraph_batch_sizes[-1]:
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            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
        else:
            num_input_tokens = num_tokens
        # copy inputs to buffer for cudagraph
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        self._set_positions(num_tokens, target_positions)
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        self.hidden_states[:num_tokens] = target_hidden_states
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        if self.supports_mm_inputs:
            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)

            self.inputs_embeds[:num_tokens] = self.model.get_input_embeddings(
                self.input_ids[:num_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
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            )
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            input_ids = None
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            inputs_embeds = self.inputs_embeds[:num_input_tokens]
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        else:
            input_ids = self.input_ids[:num_input_tokens]
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            inputs_embeds = None
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        with set_forward_context(
            per_layer_attn_metadata, self.vllm_config, num_tokens=num_input_tokens
        ):
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            ret_hidden_states = self.model(
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                input_ids=input_ids,
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                positions=self._get_positions(num_input_tokens),
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                hidden_states=self.hidden_states[:num_input_tokens],
                inputs_embeds=inputs_embeds,
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            )
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            if self.method == "mtp":
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                last_hidden_states = ret_hidden_states
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                hidden_states = last_hidden_states
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            else:
                last_hidden_states, hidden_states = ret_hidden_states
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        sample_hidden_states = last_hidden_states[last_token_indices]
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        logits = self.model.compute_logits(sample_hidden_states)
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        # Early exit if there is only one draft token to be generated.
        if self.num_speculative_tokens == 1:
            draft_token_ids = logits.argmax(dim=-1)
            return draft_token_ids.view(-1, 1)

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        if self.uses_mrope:
            positions = target_positions[:, last_token_indices]
        else:
            positions = target_positions[last_token_indices]
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        if self.method in ("deepseek_mtp", "ernie_mtp", "longcat_flash_mtp"):
            hidden_states = self.hidden_states[last_token_indices]
        else:
            hidden_states = hidden_states[last_token_indices]
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        if isinstance(attn_metadata, TreeAttentionMetadata):
            # Draft using tree attention.
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            draft_token_ids_list = self.propose_tree(
                batch_size=batch_size,
                logits=logits,
                positions=positions,
                hidden_states=hidden_states,
                common_attn_metadata=common_attn_metadata,
            )
            # [batch_size, num_tree_tokens]
            return torch.cat(draft_token_ids_list, dim=1)

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        draft_token_ids = logits.argmax(dim=-1)
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        if self.allowed_attn_types is not None and not isinstance(
            attn_metadata, self.allowed_attn_types
        ):
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            raise ValueError(
                f"Unsupported attention metadata type for speculative "
                "decoding with num_speculative_tokens > 1: "
                f"{type(attn_metadata)}. Supported types are: "
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                f"{self.allowed_attn_types}"
            )
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        # Generate the remaining draft tokens.
        draft_token_ids_list = [draft_token_ids]

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        if self.use_cuda_graph and batch_size <= self.cudagraph_batch_sizes[-1]:
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            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
        else:
            input_batch_size = batch_size
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        common_attn_metadata.num_actual_tokens = batch_size
        common_attn_metadata.max_query_len = 1
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        common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
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        common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
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            self.token_arange_np[: batch_size + 1]
        ).clone()
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        for token_index in range(self.num_speculative_tokens - 1):
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            # Update the inputs.
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            # cast to int32 is crucial when eagle model is compiled.
            # tensor.argmax() returns int64 by default.
            input_ids = draft_token_ids_list[-1].int()
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            if self.uses_mrope:
                positions += 1
                # NOTE(woosuk): We should handle the case where the draft model
                # generates tokens beyond the max model length.
                # Since it is complex to remove such requests from the batch,
                # we keep them in the batch but adjust the position ids
                # and slot mappings to avoid the
                # out-of-range access during the model execution.
                # The draft tokens generated with this adjustment
                # should be ignored.
                exceeds_max_model_len = positions[0] >= self.max_model_len
                # Mask out the position ids that exceed the max model length.
                # Otherwise, we may get out-of-range error in RoPE.
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                clamped_positions = torch.where(
                    exceeds_max_model_len.unsqueeze(0),
                    torch.zeros_like(positions),
                    positions,
                )
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            else:
                positions += 1
                exceeds_max_model_len = positions >= self.max_model_len
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                clamped_positions = torch.where(exceeds_max_model_len, 0, positions)
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            # Increment the sequence lengths.
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            common_attn_metadata.seq_lens += 1
            common_attn_metadata.seq_lens_cpu += 1
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            # For the requests that exceed the max model length, we set the
            # sequence length to 1 to minimize their overheads in attention.
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            common_attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
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            common_attn_metadata.num_computed_tokens_cpu = (
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                common_attn_metadata.seq_lens_cpu - 1
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            )
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            # Compute the slot mapping.
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            if self.uses_mrope:
                # all dimensions of positions are the same
                block_numbers = clamped_positions[0] // self.block_size
            else:
                block_numbers = clamped_positions // self.block_size
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            block_ids = common_attn_metadata.block_table_tensor.gather(
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                dim=1, index=block_numbers.view(-1, 1)
            )
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            block_ids = block_ids.view(-1)
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            if self.uses_mrope:
                common_attn_metadata.slot_mapping = (
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                    block_ids * self.block_size + clamped_positions[0] % self.block_size
                )
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            else:
                common_attn_metadata.slot_mapping = (
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                    block_ids * self.block_size + clamped_positions % self.block_size
                )
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            # Mask out the slot mappings that exceed the max model length.
            # Otherwise, the KV cache will be inadvertently updated with the
            # padding tokens.
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            common_attn_metadata.slot_mapping.masked_fill_(
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                exceeds_max_model_len, PADDING_SLOT_ID
            )
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            # Rebuild attention metadata
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            attn_metadata = attn_metadata_builder.build_for_drafting(  # type: ignore
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                common_attn_metadata=common_attn_metadata, draft_index=token_index + 1
            )
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            for layer_name in self.attn_layer_names:
                per_layer_attn_metadata[layer_name] = attn_metadata
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            # copy inputs to buffer for cudagraph
            self.input_ids[:batch_size] = input_ids
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            self._set_positions(batch_size, clamped_positions)
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            self.hidden_states[:batch_size] = hidden_states
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            if self.supports_mm_inputs:
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                self.inputs_embeds[:batch_size] = self.model.get_input_embeddings(
                    input_ids
                )
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                input_ids = None
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                inputs_embeds = self.inputs_embeds[:input_batch_size]
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            else:
                input_ids = self.input_ids[:input_batch_size]
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                inputs_embeds = None
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            # Run the model.
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            with set_forward_context(
                per_layer_attn_metadata, self.vllm_config, num_tokens=input_batch_size
            ):
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                ret_hidden_states = self.model(
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                    input_ids=input_ids,
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                    positions=self._get_positions(input_batch_size),
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                    hidden_states=self.hidden_states[:input_batch_size],
                    inputs_embeds=inputs_embeds,
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                )
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                if self.method == "mtp":
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                    last_hidden_states = ret_hidden_states
                    hidden_states = ret_hidden_states
                else:
                    last_hidden_states, hidden_states = ret_hidden_states
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            hidden_states = hidden_states[:batch_size]
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            logits = self.model.compute_logits(last_hidden_states[:batch_size])
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            draft_token_ids = logits.argmax(dim=-1)
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            draft_token_ids_list.append(draft_token_ids)

        # [batch_size, num_speculative_tokens]
        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
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        return draft_token_ids
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    def prepare_next_token_ids_cpu(
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        self,
        sampled_token_ids: list[list[int]],
        requests: dict[str, CachedRequestState],
        gpu_input_batch: InputBatch,
        num_scheduled_tokens: dict[str, int],
    ) -> torch.Tensor:
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        """
        This function is used to prepare the inputs for speculative decoding.
        It calculates the next token ids for each request based on the sampled
        token ids from the CPU. If a request has no sampled token ids (e.g.,
        during the initial decoding steps), it falls back to using the request
        state to get the next token id.
        """
        req_ids = gpu_input_batch.req_ids
        next_token_ids: list[int] = []
        for i, token_ids in enumerate(sampled_token_ids):
            if token_ids:
                # Common case.
                next_token_id = token_ids[-1]
            else:
                # Partial prefill (rare case).
                # Get the next token id from the request state.
                req_id = req_ids[i]
                req_state = requests[req_id]
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                seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
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                next_token_id = req_state.get_token_id(seq_len)
            next_token_ids.append(next_token_id)
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        next_token_ids = torch.tensor(
            next_token_ids, dtype=torch.int32, device=self.input_ids.device
        )
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        return next_token_ids

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    def prepare_next_token_ids_padded(
        self,
        common_attn_metadata: CommonAttentionMetadata,
        sampled_token_ids: torch.Tensor,
        requests: dict[str, CachedRequestState],
        gpu_input_batch: InputBatch,
        discard_request_indices: torch.Tensor,
        num_discarded_requests: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
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        """
        This function is used to prepare the inputs for speculative decoding.
        It calculates the next token ids and the number of valid sampled tokens
        for each request, considering the "discarded" requests whose next token
        is not sampled and comes from `request.get_token_id()` instead.
        It also accounts for the rejected tokens in `sampled_token_ids`.
        This function must use device functions to operate on the inputs, and
        should not introduce any blocking CPU-GPU synchronization.
        """
        # TODO(Ben): Combine this into a custom fused kernel

        # Precompute get_token_id for when there is no valid next token
        num_reqs = gpu_input_batch.num_reqs
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        self.backup_next_token_ids.np[:num_reqs] = np.array(
            [
                requests[gpu_input_batch.req_ids[i]].get_token_id(
                    common_attn_metadata.seq_lens_cpu[i].item()
                )
                for i in range(num_reqs)
            ]
        )
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        self.backup_next_token_ids.copy_to_gpu(num_reqs)

        # Mask out the sampled tokens indices that should not be sampled.
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        discard_sampled_tokens_req_indices = discard_request_indices[
            :num_discarded_requests
        ]
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        valid_sampled_token_ids_gpu = sampled_token_ids.clone()
        valid_sampled_token_ids_gpu.index_fill_(
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            0, discard_sampled_tokens_req_indices, -1
        )
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        # Generate a mask for all valid tokens within those requests
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        valid_mask = (valid_sampled_token_ids_gpu != -1) & (
            valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size
        )
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        # Count the number of valid tokens in each request
        valid_sampled_tokens_count = valid_mask.sum(dim=1)

        # Get the rightmost valid index per row
        last_valid_indices = valid_sampled_tokens_count - 1
        last_valid_indices_safe = torch.clamp(last_valid_indices, min=0)

        # Get last valid token from each row
        # (assume undefined state where there is no valid token)
        selected_tokens = torch.gather(
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            valid_sampled_token_ids_gpu, 1, last_valid_indices_safe.unsqueeze(1)
        ).squeeze(1)
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        # Use last token if valid, pre-computed backup if not
        batch_size = valid_sampled_token_ids_gpu.shape[0]
        next_token_ids = torch.where(
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            last_valid_indices != -1,
            selected_tokens,
            self.backup_next_token_ids.gpu[:batch_size],
        )
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        return next_token_ids, valid_sampled_tokens_count

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    def prepare_inputs_padded(
        self,
        common_attn_metadata: CommonAttentionMetadata,
        spec_decode_metadata: SpecDecodeMetadata,
        valid_sampled_tokens_count: torch.Tensor,
    ) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
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        """
        This function is used to prepare the inputs for speculative decoding
        It updates the common_attn_metadata for speculative decoding,
        but does not consider the rejected tokens. Instead, all tokens
        are included as inputs to the speculator, with the rejected tokens
        used as padding and filtered out later by `token_indices_to_sample`.
        No blocking CPU operations should be introduced in this function.
        """
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        num_draft_tokens_gpu = torch.cat(
            [
                spec_decode_metadata.cu_num_draft_tokens[0:1],
                spec_decode_metadata.cu_num_draft_tokens[1:]
                - spec_decode_metadata.cu_num_draft_tokens[:-1],
            ]
        )
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        num_rejected_tokens_gpu = torch.where(
            num_draft_tokens_gpu > 0,
            num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
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            torch.zeros_like(num_draft_tokens_gpu),
        )
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        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu

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        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
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        total_num_tokens = query_start_loc_cpu[-1].item()
        token_indices = self.arange[:total_num_tokens]

        spec_common_attn_metadata = CommonAttentionMetadata(
            query_start_loc=common_attn_metadata.query_start_loc,
            seq_lens=common_attn_metadata.seq_lens,
            query_start_loc_cpu=query_start_loc_cpu,
            seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
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            num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
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            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
            max_seq_len=common_attn_metadata.seq_lens_cpu.max().item(),
            block_table_tensor=common_attn_metadata.block_table_tensor,
            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
            causal=True,
        )

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        token_indices_to_sample = (
            common_attn_metadata.query_start_loc[1:] - 1 - num_rejected_tokens_gpu
        )
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        return spec_common_attn_metadata, token_indices, token_indices_to_sample

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    def propose_tree(
        self,
        batch_size: int,
        # [num_tokens, vocab_size]
        logits: torch.Tensor,
        # [num_tokens]
        positions: torch.Tensor,
        # [num_tokens, hidden_size]
        hidden_states: torch.Tensor,
        common_attn_metadata: CommonAttentionMetadata,
    ) -> list[torch.Tensor]:
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        tree_attn_metadata_builder = self.runner.attn_groups[0][
            0
        ].get_metadata_builder()
        assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
623

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        total_num_drafts = self.cu_drafts_per_level[0]
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        level_num_drafts = total_num_drafts
        # Sample a draft token for each child at the tree root level.
627
        num_children = self.child_drafts_per_level[0]
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        if num_children == 1:
            draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
        else:
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            draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                batch_size, -1
            )
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        draft_token_ids_list = [draft_token_ids]
        draft_hidden_states = hidden_states.view(batch_size, 1, -1)

        # Initialize empty tensors for concatenation with the level outputs.
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        tree_input_ids = torch.empty(
            0, device=self.input_ids.device, dtype=self.input_ids.dtype
        )
        tree_positions = torch.empty(
            0, device=self.positions.device, dtype=self.positions.dtype
        )
        tree_hidden_states = torch.empty(
            0, device=self.hidden_states.device, dtype=self.hidden_states.dtype
        )
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        # Precompute the draft token positions.
        flattened_draft_positions = (
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            positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
        )
651
        tree_depth = len(self.cu_drafts_per_level)
652
        for level in range(tree_depth - 1):
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            # Get draft positions for RoPE.
            draft_positions = positions + (level + 1)
655
            exceeds_max_model_len = (positions + total_num_drafts) >= self.max_model_len
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            # Mask out the position ids that exceed the max model length.
            # Otherwise, we may get out-of-range error in RoPE.
658
            draft_positions = torch.where(
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                exceeds_max_model_len,
                0,
                draft_positions,
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            ).view(batch_size, -1)

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            if level_num_drafts > 1:
                # Repeat the positions for each draft at this level.
666
                draft_positions = draft_positions.repeat_interleave(
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                    level_num_drafts, dim=1
                )
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            if num_children > 1:
                # Repeat draft hidden states for each child.
                draft_hidden_states = draft_hidden_states.repeat_interleave(
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                    num_children, dim=1
                )
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            # Concatenate the draft tokens, positions, and hidden states.
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            tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
            tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
679
            tree_hidden_states = torch.cat(
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                [tree_hidden_states, draft_hidden_states], dim=1
            )
682
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684

            # Build new attention metadata for the next level of drafts.
            # This is necessary to support tree attention.
685
            query_len = total_num_drafts
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            common_attn_metadata = replace(
                common_attn_metadata,
688
                query_start_loc=query_len * self.arange[: batch_size + 1],
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                seq_lens=common_attn_metadata.seq_lens + level_num_drafts,
                num_actual_tokens=batch_size * query_len,
                max_query_len=query_len,
            )
            attn_metadata = tree_attn_metadata_builder.build_for_drafting(
                common_attn_metadata=common_attn_metadata,
695
                draft_index=level + 1,
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            )

            # Apply new attention metadata to all layers.
            per_layer_attn_metadata = {}
            for layer_name in self.attn_layer_names:
                per_layer_attn_metadata[layer_name] = attn_metadata

            # Consider max model length.
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            attn_metadata.max_seq_len = min(
                attn_metadata.max_seq_len, self.max_model_len
            )
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            # For the requests that exceed the max model length, we set the
            # sequence length to 1 to minimize their overheads in attention.
            attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)

            # Compute the slot mapping.
712
            query_positions = flattened_draft_positions[:, level : level + query_len]
713
            block_numbers = query_positions // self.block_size
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            block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
            slot_mapping = (
                block_ids * self.block_size + query_positions % self.block_size
            )
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            # Mask out the slot mappings that exceed the max model length.
            # Otherwise, the KV cache will be inadvertently updated with the
            # padding tokens.
            slot_mapping[exceeds_max_model_len] = PADDING_SLOT_ID
            attn_metadata.slot_mapping = slot_mapping.view(-1)

            # Copy inputs to buffer for cudagraph.
            num_tokens = attn_metadata.num_actual_tokens
            input_ids = tree_input_ids.view(-1)
            self.input_ids[:num_tokens] = input_ids
            self.positions[:num_tokens] = tree_positions.view(-1)
729
            self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
730

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            if self.use_cuda_graph and num_tokens <= self.cudagraph_batch_sizes[-1]:
                num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
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            else:
                num_input_tokens = num_tokens
            # Run the model.
736
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738
            with set_forward_context(
                per_layer_attn_metadata, self.vllm_config, num_tokens=num_input_tokens
            ):
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                last_hidden_states, hidden_states = self.model(
                    input_ids=self.input_ids[:num_input_tokens],
                    positions=self.positions[:num_input_tokens],
                    hidden_states=self.hidden_states[:num_input_tokens],
                    inputs_embeds=None,
                )

            # Get the output hidden states for the draft tokens.
            draft_hidden_states = hidden_states[:num_tokens].view(
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749
                batch_size, query_len, -1
            )[:, -level_num_drafts:]
750
            draft_last_hidden_states = last_hidden_states[:num_tokens].view(
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                batch_size, query_len, -1
            )[:, -level_num_drafts:]
753
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755

            # Get the output logits for the draft tokens.
            logits = self.model.compute_logits(
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                draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
            )
758
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763

            # Sample a draft token for each child at the next tree level.
            num_children = self.child_drafts_per_level[level + 1]
            if num_children == 1:
                draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
            else:
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                draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                    batch_size, -1
                )
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769
            draft_token_ids_list.append(draft_token_ids)

            # Update the # drafts counters for the next tree level.
770
            level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
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773
            total_num_drafts = self.cu_drafts_per_level[level + 1]
        return draft_token_ids_list

774
    def prepare_inputs(
775
776
        self,
        common_attn_metadata: CommonAttentionMetadata,
777
778
        sampled_token_ids: list[list[int]],
        num_draft_tokens: list[int],
779
780
    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
        """
781
        This function is used to prepare the inputs for speculative decoding.
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787
        It updates to the common_attn_metadata to account for the rejected
        tokens (and newly sampled tokens). It also returns the token indices
        of the tokens that should be fed to the speculator.
        """
        # E.g.
        #  common_attn_metadata.query_start_loc{_cpu}:
788
        #       [0, q1, q1 + q2, q1 + q2 + q3]
789
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792
793
794
        #  common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
        #  num_rejected_tokens: [n1, n2, n3]
        # This function computes the intermediate values:
        #  num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
        # And returns:
        #  common_attn_metadata.query_start_loc{_cpu}:
795
        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
796
        #  common_attn_metadata.seq_lens{_cpu}:
797
        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
798
        #  token_indices: [0, 1, ..., q1 - n1 - 1,
799
800
        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
801

802
803
804
805
        num_rejected_tokens = [
            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
            for i, n in enumerate(num_draft_tokens)
        ]
806
        num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
807

808
809
        device = common_attn_metadata.query_start_loc.device
        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
810
        new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
811
812

        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
813
        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
814
815
816
817
818
819
820
821
        # [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
        new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()

        # [q1 - n1, q2 - n2, q3 - n3] ->
        # [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
        new_query_start_loc_cpu = torch.zeros(
            query_start_loc_cpu.shape,
822
            dtype=torch.int32,
823
824
            pin_memory=is_pin_memory_available(),
        )
825
826
827
828
829
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831
832
833
        new_query_start_loc_np = new_query_start_loc_cpu.numpy()
        np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])

        total_num_tokens = new_query_start_loc_np[-1]
        # Example assuming num_tokens_per_req_np = [2, 4, 3]
        # this implies that `new_query_start_locs` is:
        # [0, 2, 6, 9] ->
        # [0, 0, 2, 2, 2, 2, 6, 6, 6]
        #  _r1_  ____r2____  ___r3__
834
835
836
        new_query_start_locs_expanded = np.repeat(
            new_query_start_loc_np[:-1], new_num_tokens_per_req_np
        )
837
838
839
        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
        #  _r1_  ____r2____  ___r3__
840
841
842
        token_offests = (
            self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
        )
843
844
845
846
847
848

        # Expand starting positions to match token pattern
        # [0, q1, q1 + q2] ->
        # [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
        #  _r1_  _____r2_______  ___________r3____________
        old_query_start_locs_expanded = np.repeat(
849
850
            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
        )
851
        # Final token indices are:
852
853
854
        # [0, 1,                                // req 1
        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,       // req 2
        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
855
        token_indices_np = token_offests + old_query_start_locs_expanded
856
        token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
857
858

        spec_common_attn_metadata = CommonAttentionMetadata(
859
            query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
860
861
862
            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
            query_start_loc_cpu=new_query_start_loc_cpu,
            seq_lens_cpu=new_seq_lens_cpu,
863
            num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
864
865
866
            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
867
            max_seq_len=new_seq_lens_cpu.max().item(),
868
869
            block_table_tensor=common_attn_metadata.block_table_tensor,
            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
870
            causal=True,
871
        )
872
873

        return spec_common_attn_metadata, token_indices
874

875
    def get_model_name(self, model: nn.Module) -> str:
876
        if hasattr(model, "module"):  # multi-GPU
877
878
879
            model = model.module
        return model.__class__.__name__

880
    def load_model(self, target_model: nn.Module) -> None:
881
        draft_model_config = self.vllm_config.speculative_config.draft_model_config
882
        target_attn_layer_names = set(
883
            get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase).keys()
884
        )
885
886
        # FIXME: support hybrid kv for draft model
        target_indexer_layer_names = set(
887
888
889
890
            get_layers_from_vllm_config(
                self.vllm_config, DeepseekV32IndexerCache
            ).keys()
        )
891

892
        from vllm.compilation.backends import set_model_tag
893

894
        with set_model_tag("eagle_head"):
895
896
897
            self.model = get_model(
                vllm_config=self.vllm_config, model_config=draft_model_config
            )
898

899
        draft_attn_layer_names = (
900
            get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase).keys()
901
902
903
904
905
906
            - target_attn_layer_names
        )
        indexer_layers = get_layers_from_vllm_config(
            self.vllm_config, DeepseekV32IndexerCache
        )
        draft_indexer_layer_names = indexer_layers.keys() - target_indexer_layer_names
907
        self.attn_layer_names = list(draft_attn_layer_names)
908
909
910
911
912
        self.indexer_layer_names = list(draft_indexer_layer_names)

        if self.indexer_layer_names:
            first_layer = self.indexer_layer_names[0]
            self.draft_indexer_metadata_builder = (
913
914
915
                indexer_layers[first_layer]
                .get_attn_backend()
                .get_builder_cls()(
916
917
918
919
                    indexer_layers[first_layer].get_kv_cache_spec(),
                    self.indexer_layer_names,
                    self.vllm_config,
                    self.device,
920
921
                )
            )
922
923
        else:
            self.draft_indexer_metadata_builder = None
924

925
        if self.supports_mm_inputs:
926
927
928
            # Even if the target model is multimodal, we can also use
            # text-only draft models
            try:
929
930
931
932
                dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
                self.model.get_input_embeddings(
                    dummy_input_ids, multimodal_embeddings=None
                )
933
934
935
            except (NotImplementedError, AttributeError, TypeError):
                logger.warning(
                    "Draft model does not support multimodal inputs, "
936
937
                    "falling back to text-only mode"
                )
938
                self.supports_mm_inputs = False
939

940
941
        if supports_multimodal(target_model):
            # handle multimodality
942
943
944
945
946
            if (
                self.get_model_name(target_model)
                == "Qwen2_5_VLForConditionalGeneration"
            ):
                self.model.config.image_token_index = target_model.config.image_token_id
947
948
            else:
                self.model.config.image_token_index = (
949
950
                    target_model.config.image_token_index
                )
951
952
953
            target_language_model = target_model.get_language_model()
        else:
            target_language_model = target_model
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        # share embed_tokens with the target model if needed
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        if get_pp_group().world_size == 1:
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            if hasattr(target_language_model.model, "embed_tokens"):
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                target_embed_tokens = target_language_model.model.embed_tokens
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            elif hasattr(target_language_model.model, "embedding"):
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                target_embed_tokens = target_language_model.model.embedding
            else:
                raise AttributeError(
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                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
                )
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            # Check if shapes match and we found the embedding
            eagle_shape = self.model.model.embed_tokens.weight.shape
            target_shape = target_embed_tokens.weight.shape
            if eagle_shape == target_shape:
                logger.info(
                    "Assuming the EAGLE head shares the same vocab embedding"
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                    " with the target model."
                )
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                del self.model.model.embed_tokens
                self.model.model.embed_tokens = target_embed_tokens
            else:
                logger.info(
                    "The EAGLE head's vocab embedding will be loaded separately"
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                    " from the target model."
                )
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        else:
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            logger.info(
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                "The EAGLE head's vocab embedding will be loaded separately"
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                " from the target model."
            )
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        # share lm_head with the target model if needed
        # some model definition do not define lm_head explicitly
        # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
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        if self.vllm_config.speculative_config.method != "eagle3":
            if hasattr(target_language_model, "lm_head"):
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                logger.info("Loading EAGLE LM head weights from the target model.")
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                self.model.lm_head = target_language_model.lm_head
        else:
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            if (
                hasattr(self.model, "lm_head")
                and hasattr(target_language_model, "lm_head")
                and self.model.lm_head.weight.shape
                == target_language_model.lm_head.weight.shape
            ):
                logger.info(
                    "Assuming the EAGLE head shares the same lm_head"
                    " with the target model."
                )
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                del self.model.lm_head
                self.model.lm_head = target_language_model.lm_head
            else:
                logger.info(
                    "The EAGLE head's lm_head will be loaded separately"
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                    " from the target model."
                )
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    @torch.inference_mode()
    def dummy_run(
        self,
        num_tokens: int,
    ) -> None:
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        with set_forward_context(None, self.vllm_config, num_tokens=num_tokens):
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            if self.supports_mm_inputs:
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                input_ids = None
                inputs_embeds = self.inputs_embeds[:num_tokens]
            else:
                input_ids = self.input_ids[:num_tokens]
                inputs_embeds = None

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            self.model(
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                input_ids=input_ids,
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                positions=self._get_positions(num_tokens),
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                hidden_states=self.hidden_states[:num_tokens],
                inputs_embeds=inputs_embeds,
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            )
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    def _get_attention_metadata_builder(self) -> list[AttentionMetadataBuilder]:
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        """Find and return the attention metadata builders for EAGLE layers.
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        Returns:
            The metadata builders for EAGLE layers.
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        Raises:
            AssertionError: If no metadata builders are found for EAGLE layers.
        """
        builder = None
        chosen_layer = self.attn_layer_names[0]

        for kv_cache_group in self.runner.attn_groups:
            for attn_group in kv_cache_group:
                if chosen_layer in attn_group.layer_names:
                    builder = attn_group.get_metadata_builder()
                    break
            if builder is not None:
                break

        assert builder is not None, (
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            "Failed to find attention metadata builder for EAGLE layers."
        )
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        return builder

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    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
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        """
        Validate that all eagle layers belong to the same KVCacheGroup.
        Need this assumption to ensure all eagle layers can use the
        same AttentionMetadata.
        May extend to multiple AttentionMetadata in the future.
        """
        kv_cache_groups: dict[str, int] = {}
        for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
            for layer_name in kv_cache_group.layer_names:
                kv_cache_groups[layer_name] = id
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        assert (
            len(
                set(
                    [
                        kv_cache_groups[layer_name]
                        for layer_name in self.attn_layer_names
                    ]
                )
            )
            == 1
        ), "All eagle layers should belong to the same kv cache group"
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# NOTE(woosuk): Currently, the below code is not used and we always use argmax
# to sample the draft tokens. We will use this after we find a way to manage
# the draft prob tensor.
# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
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# FIXME(woosuk): The logic here is duplicated with the main sampling code.
# We should refactor this to reuse the same sampling implementation.
def compute_probs_and_sample_next_token(
    logits: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> tuple[torch.Tensor, torch.Tensor]:
    if sampling_metadata.all_greedy:
        # For greedy requests, draft_probs is not used in rejection sampling.
        # Therefore, we can just return the logits.
        probs = logits
        next_token_ids = logits.argmax(dim=-1)
        return next_token_ids, probs

    is_greedy = sampling_metadata.temperature == -1
    temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
    logits.div_(temperature.view(-1, 1))
    probs = logits.softmax(dim=-1, dtype=torch.float32)

    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
    # generating the draft tokens. We only use the temperature. While this
    # could degrade the acceptance rate, it does not affect the distribution
    # of the generated tokens after rejection sampling.

    # TODO(woosuk): Consider seeds.
    q = torch.empty_like(probs)
    q.exponential_()
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    # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
    # will be used later for rejection sampling.
    next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
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    if not sampling_metadata.all_random:
        greedy_token_ids = probs.argmax(dim=-1)
        next_token_ids = torch.where(
            is_greedy,
            greedy_token_ids,
            next_token_ids,
        )
    return next_token_ids, probs