eagle.py 78.4 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
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from importlib.util import find_spec
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from typing import Any, cast
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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 (
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
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    replace,
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)
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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_eagle3 import Eagle3DeepseekV2ForCausalLM
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from vllm.model_executor.models.interfaces import SupportsMultiModal
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.model_executor.models.qwen3_dflash import DFlashQwen3ForCausalLM
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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.triton_utils import triton
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.v1.attention.backend import CommonAttentionMetadata
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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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.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import KVCacheConfig, UniformTypeKVCacheSpecs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.sampler import _SAMPLING_EPS
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.utils import (
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    PADDING_SLOT_ID,
    compute_new_slot_mapping,
    copy_and_expand_eagle_inputs_kernel,
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    eagle_prepare_inputs_padded_kernel,
    eagle_prepare_next_token_padded_kernel,
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    eagle_step_update_slot_mapping_and_metadata,
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    extend_all_queries_by_N,
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)
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from vllm.v1.utils import CpuGpuBuffer
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.utils import AttentionGroup
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logger = init_logger(__name__)

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class SpecDecodeBaseProposer:
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    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
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        pass_hidden_states_to_model: bool,
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        runner=None,
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    ):
        self.vllm_config = vllm_config
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        assert vllm_config.speculative_config is not None
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        self.speculative_config = vllm_config.speculative_config
        self.draft_model_config = self.speculative_config.draft_model_config
        self.method = self.speculative_config.method
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        self.pass_hidden_states_to_model = pass_hidden_states_to_model
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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
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        self.dp_rank = vllm_config.parallel_config.data_parallel_rank
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        self.num_speculative_tokens = self.speculative_config.num_speculative_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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        self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
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        # Unifying eagle, draft model, and parallel drafting support.
        # DFlash always uses parallel drafting (all tokens in one pass),
        # but has an additional slot for the next_token_id (does not shift like EAGLE)
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        self.parallel_drafting: bool = self.speculative_config.parallel_drafting
        self.extra_slots_per_request = (
            1 if not self.parallel_drafting else self.num_speculative_tokens
        )
        self.net_num_new_slots_per_request = self.extra_slots_per_request - (
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            1 if (self.pass_hidden_states_to_model and self.method != "dflash") else 0
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        )
        self.needs_extra_input_slots = self.net_num_new_slots_per_request > 0

        self.parallel_drafting_token_id: int = 0
        self.parallel_drafting_hidden_state_tensor: torch.Tensor | None = None
        if self.parallel_drafting:
            self._init_parallel_drafting_params()
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        self.use_local_argmax_reduction: bool = (
            self.speculative_config.use_local_argmax_reduction
        )
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        self.max_batch_size = vllm_config.scheduler_config.max_num_seqs
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        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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        # Can be specialized by methods like DFlash to reduce the limit
        self.max_query_tokens = self.max_num_tokens
        self.max_positions = self.max_num_tokens

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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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        )
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        self.draft_attn_groups: list[AttentionGroup] = []
        self.kv_cache_gid: int = -1
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        self.eagle3_use_aux_hidden_state: bool = (
            self._get_eagle3_use_aux_hidden_state_from_config()
        )
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        self.compilation_config = self.vllm_config.compilation_config
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        # Cudagraph dispatcher for PIECEWISE-only dispatching in eagle.
        # Keys are initialized later via initialize_cudagraph_keys() called from
        # gpu_model_runner._check_and_update_cudagraph_mode after
        # adjust_cudagraph_sizes_for_spec_decode is called.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)
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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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        # Use draft model's M-RoPE setting, not target model's
        # Draft models may be text-only even if target is multimodal
        self.uses_mrope = self.draft_model_config.uses_mrope
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        self.uses_xdrope_dim = self.vllm_config.model_config.uses_xdrope_dim
        self.draft_uses_xdrope_dim = self.draft_model_config.uses_xdrope_dim
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923

            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = torch.zeros(
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                (3, self.max_positions + 1), dtype=torch.int64, device=device
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            )
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        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
            self.xdrope_positions = torch.zeros(
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                (self.uses_xdrope_dim, self.max_positions + 1),
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                dtype=torch.int64,
                device=device,
            )
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        else:
            # RoPE need (max_num_tokens,)
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            self.positions = torch.zeros(
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                self.max_positions,
                dtype=torch.int64,
                device=device,
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            )
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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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        # Will be set when we initialize the attention backend
        self.block_size: int = -1

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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_num_slots_for_arange = max(self.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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        if self.needs_extra_input_slots:
            self._raise_if_padded_drafter_batch_disabled()
            self._raise_if_multimodal()
            self._raise_if_mrope()

        self.is_rejected_token_mask: torch.Tensor | None = None
        self.is_masked_token_mask: torch.Tensor | None = None
        if self.needs_extra_input_slots:
            # For draft models and parallel drafting, we need to keep track of
            # which tokens are rejected to update the slot mapping with padding slots.
            self.is_rejected_token_mask = torch.zeros(
                (self.max_num_tokens,), dtype=torch.bool, device=device
            )
            # For parallel drafting, we also need to keep track of which tokens
            # are parallel-padding tokens used to sample at later positions.
            # We populate this tensor even when using draft models for simplicity.
            self.is_masked_token_mask = torch.zeros(
                (self.max_num_tokens,), dtype=torch.bool, device=device
            )

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        self.inputs_embeds = torch.zeros(
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            (self.max_num_tokens, self.inputs_embeds_size),
            dtype=self.dtype,
            device=device,
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        )
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        self.backup_next_token_ids = CpuGpuBuffer(
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            self.max_batch_size,
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            dtype=torch.int32,
            pin_memory=is_pin_memory_available(),
            device=device,
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            with_numpy=True,
        )
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        self._slot_mapping_buffer = torch.zeros(
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            self.max_positions,
            dtype=torch.int64,
            device=device,
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        )

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        # Determine allowed attention backends once during initialization.
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        self.allowed_attn_types: tuple | None = None
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        if current_platform.is_rocm():
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            from vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse import (
                ROCMAiterMLASparseMetadata,
            )
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            from vllm.v1.attention.backends.rocm_attn import RocmAttentionMetadata

            rocm_types = [
                TritonAttentionMetadata,
                RocmAttentionMetadata,
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                ROCMAiterMLASparseMetadata,
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            ]
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            # ROCM_AITER_FA is an optional backend
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            # We check is_enabled() here to avoid importing the backend module during
            # auto-discovery when VLLM_ROCM_USE_AITER=0, which would trigger aiter
            # import and JIT compilation warnings. Explicit backend selection via
            # attention_config still works because the backend module is loaded
            # directly when selected, not through this auto-discovery path.
            # Check if backend module exists to allow explicit selection
            if find_spec(
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                AttentionBackendEnum.ROCM_AITER_FA.get_path(include_classname=False)
            ):
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                from vllm.v1.attention.backends.rocm_aiter_fa import (
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                    AiterFlashAttentionMetadata,
                )

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                rocm_types.append(AiterFlashAttentionMetadata)
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            # TRITON_MLA backend support for MLA models (e.g., DeepSeek)
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            from vllm.model_executor.layers.attention.mla_attention import (
                MLACommonMetadata,
            )
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            rocm_types.append(MLACommonMetadata)

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            # FlexAttention backend support
            from vllm.v1.attention.backends.flex_attention import FlexAttentionMetadata

            rocm_types.append(FlexAttentionMetadata)

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            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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        assert spec_token_tree is not None
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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(
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            1, len(self.tree_choices) + 1, device=device, dtype=torch.int32
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        ).repeat(self.max_batch_size, 1)
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    def _raise_if_padded_drafter_batch_disabled(self):
        if self.speculative_config.disable_padded_drafter_batch:
            raise NotImplementedError(
                "Speculative Decoding with draft models or parallel drafting only "
                "supports padded drafter batch. Please unset "
                "disable_padded_drafter_batch in the speculative_config."
            )

    def _raise_if_multimodal(self):
        if self.supports_mm_inputs:
            raise NotImplementedError(
                "Speculative Decoding with draft models or parallel drafting "
                "does not support multimodal models yet"
            )

    def _raise_if_mrope(self):
        if self.draft_model_config.uses_mrope:
            raise NotImplementedError(
                "Speculative Decoding with draft models or parallel drafting "
                "does not support M-RoPE yet"
            )

    def _init_parallel_drafting_params(self):
        # For parallel drafting, we need the token ID to use for masked slots
        # And for EAGLE + parallel drafting, we need the hidden state tensor to use
        # for those masked slots.

        model_hf_config = self.draft_model_config.hf_config
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        # DFlash stores mask_token_id in dflash_config
        dflash_config = getattr(model_hf_config, "dflash_config", None)
        if dflash_config and "mask_token_id" in dflash_config:
            self.parallel_drafting_token_id = dflash_config["mask_token_id"]
        elif hasattr(model_hf_config, "pard_token"):
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            self.parallel_drafting_token_id = model_hf_config.pard_token
        elif hasattr(model_hf_config, "ptd_token_id"):
            self.parallel_drafting_token_id = model_hf_config.ptd_token_id
        else:
            raise ValueError(
                "For parallel drafting, the draft model config must have "
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                "`pard_token`, `ptd_token_id`, or "
                "`dflash_config.mask_token_id` specified in its config.json."
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            )

        if self.pass_hidden_states_to_model:
            self.parallel_drafting_hidden_state_tensor = torch.empty(
                self.hidden_size, dtype=self.dtype, device=self.device
            )

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    def _get_positions(self, num_tokens: int):
        if self.uses_mrope:
            return self.mrope_positions[:, :num_tokens]
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        if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
            return self.xdrope_positions[:, :num_tokens]
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        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
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        elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
            self.xdrope_positions[:, :num_tokens] = positions
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        else:
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            # Convert M-RoPE positions if target model uses M-RoPE
            # but draft doesn't, For text inputs, all M-RoPE
            # dimensions are identical
            if self.vllm_config.model_config.uses_mrope:
                positions = positions[0]
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            self.positions[:num_tokens] = positions

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    def _get_slot_mapping(
        self,
        num_tokens: int,
        slot_mapping: torch.Tensor | None = None,
    ) -> dict[str, torch.Tensor]:
        """Return slot_mapping dict for EAGLE layers.

        If slot_mapping is provided, copies it into the buffer first.
        """
        if slot_mapping is not None:
            num_actual = slot_mapping.shape[0]
            self._slot_mapping_buffer[:num_actual].copy_(slot_mapping)
            if num_tokens > num_actual:
                self._slot_mapping_buffer[num_actual:num_tokens].fill_(PADDING_SLOT_ID)

        view = self._slot_mapping_buffer[:num_tokens]
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        return {name: view for name in self._draft_attn_layer_names}
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    def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode) -> None:
        """Initialize cudagraph dispatcher keys for eagle.

        Eagle only supports PIECEWISE cudagraphs (via mixed_mode).
        This should be called after adjust_cudagraph_sizes_for_spec_decode.
        """
        if (
            not self.speculative_config.enforce_eager
            and cudagraph_mode.mixed_mode()
            in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]
        ):
            eagle_cudagraph_mode = CUDAGraphMode.PIECEWISE
        else:
            eagle_cudagraph_mode = CUDAGraphMode.NONE

        self.cudagraph_dispatcher.initialize_cudagraph_keys(eagle_cudagraph_mode)

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    def _greedy_sample(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Greedy-sample draft tokens from hidden states."""
        if self.use_local_argmax_reduction:
            return self.model.get_top_tokens(hidden_states)
        return self.model.compute_logits(hidden_states).argmax(dim=-1)

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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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        token_indices_to_sample: torch.Tensor | None,
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        common_attn_metadata: CommonAttentionMetadata,
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        sampling_metadata: SamplingMetadata,
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        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
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        num_rejected_tokens_gpu: torch.Tensor | None = None,
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        slot_mappings: dict[str, torch.Tensor]
        | list[dict[str, torch.Tensor]]
        | None = None,
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    ) -> torch.Tensor:
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        batch_size = common_attn_metadata.batch_size()
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        if self.method in ("eagle3", "dflash"):
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            assert isinstance(
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                self.model,
                (
                    Eagle3LlamaForCausalLM,
                    Eagle3DeepseekV2ForCausalLM,
                    DFlashQwen3ForCausalLM,
                ),
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            )
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            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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        num_tokens, token_indices_to_sample, common_attn_metadata = (
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            self.set_inputs_first_pass(
                target_token_ids=target_token_ids,
                next_token_ids=next_token_ids,
                target_positions=target_positions,
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                target_hidden_states=target_hidden_states,
                token_indices_to_sample=token_indices_to_sample,
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                cad=common_attn_metadata,
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
            )
        )
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        per_layer_attn_metadata = self.build_per_layer_attn_metadata(
            common_attn_metadata
        )
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        cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
            self._determine_batch_execution_and_padding(num_tokens)
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        )

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        model_kwargs, slot_mapping_size = self.build_model_inputs_first_pass(
            num_tokens, num_input_tokens, mm_embed_inputs
        )
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        with set_forward_context(
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            per_layer_attn_metadata,
            self.vllm_config,
            num_tokens=num_input_tokens,
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            num_tokens_across_dp=num_tokens_across_dp,
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            cudagraph_runtime_mode=cudagraph_runtime_mode,
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            slot_mapping=self._get_slot_mapping(
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                slot_mapping_size, common_attn_metadata.slot_mapping
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            ),
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        ):
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            ret_hidden_states = self.model(**model_kwargs)
            if not self.model_returns_tuple():
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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[token_indices_to_sample]
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        # Early exit if there is only one draft token to be generated.
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        if self.num_speculative_tokens == 1 or self.parallel_drafting:
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            draft_token_ids = self._greedy_sample(sample_hidden_states)
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            return draft_token_ids.view(-1, self.num_speculative_tokens)
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        if self.uses_mrope:
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            positions = self.mrope_positions[:, token_indices_to_sample]
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        else:
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            positions = self.positions[token_indices_to_sample]
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        hidden_states = hidden_states[token_indices_to_sample]
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        if any(
            isinstance(attn_metadata, TreeAttentionMetadata)
            for attn_metadata in per_layer_attn_metadata.values()
        ):
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            # Draft using tree attention - requires full logits for top-k
            logits = self.model.compute_logits(sample_hidden_states)
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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,
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                slot_mappings=slot_mappings,
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            )
            # [batch_size, num_tree_tokens]
            return torch.cat(draft_token_ids_list, dim=1)

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        draft_token_ids = self._greedy_sample(sample_hidden_states)
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        for attn_metadata in per_layer_attn_metadata.values():
            if self.allowed_attn_types is not None and not isinstance(
                attn_metadata, self.allowed_attn_types
            ):
                raise ValueError(
                    f"Unsupported attention metadata type for speculative "
                    "decoding with num_speculative_tokens > 1: "
                    f"{type(attn_metadata)}. Supported types are: "
                    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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        cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
            self._determine_batch_execution_and_padding(batch_size)
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        )
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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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        # In padded drafter batch, we need to adjust the sequence lengths
        # to remove the "padding" (i.e. rejected tokens).
        # Only apply this adjustment when we have rejected tokens
        # (i.e., not the first proposal).
        if self.num_speculative_tokens > 1 and num_rejected_tokens_gpu is not None:
            common_attn_metadata.seq_lens -= num_rejected_tokens_gpu
            # Invalidate the CPU-side shadows to avoid H<>D sync.
            common_attn_metadata._seq_lens_cpu = None
            common_attn_metadata._num_computed_tokens_cpu = None

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        block_size = self.block_size
        assert block_size > 0, "block_size has not been initialized."
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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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            # Use fused kernel for slot mapping and metadata updates.
            # Write clamped positions directly into the positions buffer to
            # avoid an extra D2D copy for the common (non-mrope) case.
            positions_1d = positions[0] if self.uses_mrope else positions
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            if self.uses_mrope:
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                out_pos = self.mrope_positions[0, :batch_size]
            elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
                out_pos = self.xdrope_positions[0, :batch_size]
            else:
                out_pos = self.positions[:batch_size]
            eagle_step_update_slot_mapping_and_metadata(
                positions_1d=positions_1d,
                block_table_tensor=common_attn_metadata.block_table_tensor,
                seq_lens=common_attn_metadata.seq_lens,
                block_size=block_size,
                max_model_len=self.max_model_len,
                out_clamped_positions=out_pos,
                out_slot_mapping=self._slot_mapping_buffer[:input_batch_size],
                input_batch_size=input_batch_size,
            )
            common_attn_metadata.slot_mapping = self._slot_mapping_buffer[:batch_size]
            if self.uses_mrope:
                self.mrope_positions[1:, :batch_size] = self.mrope_positions[
                    0, :batch_size
                ]
                positions = self.mrope_positions[:, :batch_size]
            elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
                self.xdrope_positions[1:, :batch_size] = self.xdrope_positions[
                    0, :batch_size
                ]
                positions = self.xdrope_positions[0, :batch_size]
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            else:
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                positions = self.positions[:batch_size]
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            # Increment the maximum sequence length. We increment max_seq_len
            # unconditionally even though some seq_lens may have been capped above,
            # as max_seq_len serves as an upper bound for sequence lengths.
            common_attn_metadata.max_seq_len = min(
                common_attn_metadata.max_seq_len + 1, self.max_model_len
            )
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            # Also update the CPU-side shadow; NOTE: this is hacky and should be
            # removed in when common_attn_metadata.seq_lens_cpu is deprecated.
            if common_attn_metadata._seq_lens_cpu is not None:
                common_attn_metadata._seq_lens_cpu += 1
            if common_attn_metadata._num_computed_tokens_cpu is not None:
                common_attn_metadata._num_computed_tokens_cpu += 1
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            # Rebuild attention metadata
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            per_layer_attn_metadata = self.build_per_layer_attn_metadata(
                common_attn_metadata, draft_index=token_index + 1
            )
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            # copy inputs to buffer for cudagraph
            self.input_ids[:batch_size] = input_ids
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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.embed_input_ids(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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            model_kwargs = {
                "input_ids": input_ids,
                "positions": self._get_positions(input_batch_size),
                "inputs_embeds": inputs_embeds,
            }
            if self.pass_hidden_states_to_model:
                model_kwargs["hidden_states"] = self.hidden_states[:input_batch_size]

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            with set_forward_context(
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                per_layer_attn_metadata,
                self.vllm_config,
                num_tokens=input_batch_size,
Rémi Delacourt's avatar
Rémi Delacourt committed
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                num_tokens_across_dp=batch_size_across_dp,
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                cudagraph_runtime_mode=cudagraph_runtime_mode,
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                slot_mapping=self._get_slot_mapping(input_batch_size),
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            ):
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                ret_hidden_states = self.model(**model_kwargs)
                if not self.model_returns_tuple():
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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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            draft_token_ids = self._greedy_sample(last_hidden_states[:batch_size])
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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 set_inputs_first_pass(
        self,
        target_token_ids: torch.Tensor,
        next_token_ids: torch.Tensor,
        target_positions: torch.Tensor,
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        target_hidden_states: torch.Tensor,
        token_indices_to_sample: torch.Tensor | None,
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        cad: CommonAttentionMetadata,
        num_rejected_tokens_gpu: torch.Tensor | None,
    ) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
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        if not self.needs_extra_input_slots:
            # Default EAGLE pathway: no reshaping of input tensors needed.
            # Simply rotate the input ids and leave the positions unchanged,
            # Inserting the next token ids at the last slot in each request.
            if token_indices_to_sample is None:
                token_indices_to_sample = cad.query_start_loc[1:] - 1

            num_tokens = target_token_ids.shape[0]
            # Shift the input ids by one token.
            # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
            self.input_ids[: num_tokens - 1] = target_token_ids[1:]
            # Replace the last token with the next token.
            # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
            self.input_ids[token_indices_to_sample] = next_token_ids

            # copy inputs to buffer for cudagraph
            if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim == 0:
                target_positions = target_positions[0]
            self._set_positions(num_tokens, target_positions)

            self.hidden_states[:num_tokens] = target_hidden_states

            return num_tokens, token_indices_to_sample, cad
        else:
            assert self.is_rejected_token_mask is not None
            assert self.is_masked_token_mask is not None
            # 1.
            # Call a custom triton kernel to copy input_ids and positions
            # into the correct slots in the preallocated buffers self.input_ids,
            # self.positions.
            batch_size = cad.batch_size()
            # Since we might have to copy a lot of data for prefills, we select the
            # block size based on the max query length and limit to max 256 slots/block.
            max_num_tokens_per_request = (
                cad.max_query_len + self.net_num_new_slots_per_request
            )
            BLOCK_SIZE_TOKENS = min(
                256, triton.next_power_of_2(max_num_tokens_per_request)
            )
            num_blocks = (
                max_num_tokens_per_request + BLOCK_SIZE_TOKENS - 1
            ) // BLOCK_SIZE_TOKENS
            total_num_input_tokens = target_token_ids.shape[0]
            total_num_output_tokens = total_num_input_tokens + (
                self.net_num_new_slots_per_request * batch_size
            )
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            token_indices_to_sample = torch.empty(
                batch_size * self.extra_slots_per_request,
                dtype=torch.int32,
                device=self.device,
            )
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            # Destination indices to write target_hidden_states into drafting buffer.
            out_hidden_state_mapping = torch.empty(
                total_num_input_tokens, dtype=torch.int32, device=self.device
            )

            # Kernel grid: one program per request (row)
            grid = (batch_size, num_blocks)
            query_start_loc = cad.query_start_loc
            query_end_loc = cad.query_start_loc[1:] - 1
            if num_rejected_tokens_gpu is not None:
                query_end_loc = query_end_loc - num_rejected_tokens_gpu
            copy_and_expand_eagle_inputs_kernel[grid](
                # (Padded) Inputs from the target model
                target_token_ids_ptr=target_token_ids,
                target_positions_ptr=target_positions,
                next_token_ids_ptr=next_token_ids,  # sampled tokens, one per request
                # Outputs to the drafting buffers
                out_input_ids_ptr=self.input_ids,
                out_positions_ptr=self.positions,  # Doesn't support mrope for now
                out_is_rejected_token_mask_ptr=self.is_rejected_token_mask,
                out_is_masked_token_mask_ptr=self.is_masked_token_mask,
                out_new_token_indices_ptr=token_indices_to_sample,
                out_hidden_state_mapping_ptr=out_hidden_state_mapping,
                # Input metadata
                query_start_loc_ptr=query_start_loc,
                query_end_loc_ptr=query_end_loc,
                padding_token_id=0,
                parallel_drafting_token_id=self.parallel_drafting_token_id,
                # Sizing info
                # Note that we can deduce batch_size for free from the grid size
                total_input_tokens=total_num_input_tokens,
                num_padding_slots_per_request=self.extra_slots_per_request,
                shift_input_ids=self.pass_hidden_states_to_model,
                BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
            )
            if self.pass_hidden_states_to_model:
                assert self.parallel_drafting_hidden_state_tensor is not None
                self.hidden_states[out_hidden_state_mapping] = target_hidden_states
                # Use torch.where to avoid DtoH sync from boolean indexing
                mask = self.is_masked_token_mask[:total_num_output_tokens]
                torch.where(
                    mask.unsqueeze(1),
                    self.parallel_drafting_hidden_state_tensor,
                    self.hidden_states[:total_num_output_tokens],
                    out=self.hidden_states[:total_num_output_tokens],
                )

            # 2.
            # Recompute the slot mapping based on the new positions and
            # rejection mask.
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            assert self.block_size > 0, "block_size has not been initialized."
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            new_slot_mapping = compute_new_slot_mapping(
                cad=cad,
                new_positions=self.positions[:total_num_output_tokens],
                is_rejected_token_mask=self.is_rejected_token_mask[
                    :total_num_output_tokens
                ],
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                block_size=self.block_size,
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                num_new_tokens=self.net_num_new_slots_per_request,
                max_model_len=self.max_model_len,
            )

            # 3. Update the common attention metadata with the new (meta)data
            new_cad = extend_all_queries_by_N(
                cad,
                N=self.net_num_new_slots_per_request,
                arange=self.arange,
                new_slot_mapping=new_slot_mapping,
            )
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            return total_num_output_tokens, token_indices_to_sample, new_cad
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    def build_model_inputs_first_pass(
        self,
        num_tokens: int,
        num_input_tokens: int,
        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None,
    ) -> tuple[dict[str, Any], int]:
        if self.supports_mm_inputs:
            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)

            self.inputs_embeds[:num_tokens] = self.model.embed_input_ids(
                self.input_ids[:num_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
            )

            input_ids = None
            inputs_embeds = self.inputs_embeds[:num_input_tokens]
        else:
            input_ids = self.input_ids[:num_input_tokens]
            inputs_embeds = None

        model_kwargs = {
            "input_ids": input_ids,
            "positions": self._get_positions(num_input_tokens),
            "inputs_embeds": inputs_embeds,
        }
        if self.pass_hidden_states_to_model:
            model_kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]

        return model_kwargs, num_input_tokens

    def build_per_layer_attn_metadata(
        self, common_attn_metadata: CommonAttentionMetadata, draft_index: int = 0
    ) -> dict[str, object]:
        per_layer_attn_metadata: dict[str, object] = {}
        for attn_group in self.draft_attn_groups:
            attn_metadata = attn_group.get_metadata_builder().build_for_drafting(
                common_attn_metadata=common_attn_metadata, draft_index=draft_index
            )
            for layer_name in attn_group.layer_names:
                per_layer_attn_metadata[layer_name] = attn_metadata
        return per_layer_attn_metadata

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    def model_returns_tuple(self) -> bool:
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        return self.method not in ("mtp", "draft_model", "dflash")
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    def prepare_next_token_ids_cpu(
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        self,
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        sampled_token_ids: list[list[int]],
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        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):
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            if token_ids:
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                # 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(
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            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,
        sampled_token_ids: torch.Tensor,
        requests: dict[str, CachedRequestState],
        gpu_input_batch: InputBatch,
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        discard_request_mask: torch.Tensor,
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    ) -> 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
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        is not sampled and comes from `request.get_token_id()` instead. This is denoted
        the "backup" token id. It also counts rejected tokens via `sampled_token_ids`.
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        """
        # Precompute get_token_id for when there is no valid next token
        num_reqs = gpu_input_batch.num_reqs
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        seq_lens_list = (gpu_input_batch.num_tokens_no_spec[:num_reqs] - 1).tolist()
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        self.backup_next_token_ids.np[:num_reqs] = np.array(
            [
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                requests[gpu_input_batch.req_ids[i]].get_token_id(seq_lens_list[i])
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                for i in range(num_reqs)
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            ],
            dtype=np.int32,
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        )
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        self.backup_next_token_ids.copy_to_gpu(num_reqs)
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        backup_tokens_gpu = self.backup_next_token_ids.gpu
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        batch_size, num_tokens = sampled_token_ids.shape
        device = sampled_token_ids.device
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        assert discard_request_mask.dtype == torch.bool
        assert backup_tokens_gpu.dtype == torch.int32
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        next_token_ids = torch.empty(batch_size, dtype=torch.int32, device=device)
        valid_sampled_tokens_count = next_token_ids.new_empty(batch_size)
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        # Kernel grid: one program per request (row)
        grid = (batch_size,)

        # Find the next power of 2 for block sizes
        BLOCK_SIZE_TOKENS = triton.next_power_of_2(num_tokens)
        eagle_prepare_next_token_padded_kernel[grid](
            sampled_token_ids,
            discard_request_mask,
            backup_tokens_gpu,
            next_token_ids,
            valid_sampled_tokens_count,
            gpu_input_batch.vocab_size,
            num_tokens,
            batch_size,
            sampled_token_ids.stride(0),
            BLOCK_SIZE_TOKENS=BLOCK_SIZE_TOKENS,
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        )
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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,
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    ) -> 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`.
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        """
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        num_reqs = common_attn_metadata.num_reqs
        device = valid_sampled_tokens_count.device

        token_indices_to_sample = torch.empty(
            (num_reqs,), dtype=torch.int32, device=device
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        )
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        num_rejected_tokens_gpu = torch.empty(
            (num_reqs,), dtype=torch.int32, device=device
        )
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        grid = (num_reqs,)
        eagle_prepare_inputs_padded_kernel[grid](
            spec_decode_metadata.cu_num_draft_tokens,
            valid_sampled_tokens_count,
            common_attn_metadata.query_start_loc,
            token_indices_to_sample,
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            num_rejected_tokens_gpu,
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            num_reqs,
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        )
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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()

        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,
959
960
            _seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
961
962
963
            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
964
            max_seq_len=common_attn_metadata.max_seq_len,
965
            block_table_tensor=common_attn_metadata.block_table_tensor,
966
            slot_mapping=common_attn_metadata.slot_mapping[:total_num_tokens],
967
            causal=True,
968
            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
969
970
        )

971
972
973
974
975
        return (
            spec_common_attn_metadata,
            token_indices_to_sample,
            num_rejected_tokens_gpu,
        )
976

977
978
979
980
981
982
983
984
985
986
    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,
987
988
989
        slot_mappings: dict[str, torch.Tensor]
        | list[dict[str, torch.Tensor]]
        | None = None,
990
    ) -> list[torch.Tensor]:
991
        tree_attn_metadata_builder = self.draft_attn_groups[0].get_metadata_builder()
992
        assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
993

994
        total_num_drafts = self.cu_drafts_per_level[0]
995
996
        level_num_drafts = total_num_drafts
        # Sample a draft token for each child at the tree root level.
997
        num_children = self.child_drafts_per_level[0]
998
999
1000
        if num_children == 1:
            draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
        else:
1001
1002
1003
            draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                batch_size, -1
            )
1004
1005
1006
1007
        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.
1008
1009
1010
1011
1012
1013
1014
1015
1016
        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
        )
1017
1018
        # Precompute the draft token positions.
        flattened_draft_positions = (
1019
1020
            positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
        )
1021
        tree_depth = len(self.cu_drafts_per_level)
1022
        for level in range(tree_depth - 1):
1023
1024
            # Get draft positions for RoPE.
            draft_positions = positions + (level + 1)
1025
            exceeds_max_model_len = (positions + total_num_drafts) >= self.max_model_len
1026
1027
            # Mask out the position ids that exceed the max model length.
            # Otherwise, we may get out-of-range error in RoPE.
1028
            draft_positions = torch.where(
1029
1030
1031
                exceeds_max_model_len,
                0,
                draft_positions,
1032
1033
            ).view(batch_size, -1)

1034
1035
            if level_num_drafts > 1:
                # Repeat the positions for each draft at this level.
1036
                draft_positions = draft_positions.repeat_interleave(
1037
1038
                    level_num_drafts, dim=1
                )
1039
1040
1041
1042

            if num_children > 1:
                # Repeat draft hidden states for each child.
                draft_hidden_states = draft_hidden_states.repeat_interleave(
1043
1044
                    num_children, dim=1
                )
1045
1046

            # Concatenate the draft tokens, positions, and hidden states.
1047
1048
            tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
            tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
1049
            tree_hidden_states = torch.cat(
1050
1051
                [tree_hidden_states, draft_hidden_states], dim=1
            )
1052
1053
1054

            # Build new attention metadata for the next level of drafts.
            # This is necessary to support tree attention.
1055
            query_len = total_num_drafts
1056
1057
            common_attn_metadata = replace(
                common_attn_metadata,
1058
                query_start_loc=query_len * self.arange[: batch_size + 1],
1059
1060
1061
1062
1063
                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(
1064
                common_attn_metadata=common_attn_metadata, draft_index=level + 1
1065
1066
            )

1067
            # Apply new attention metadata to all draft layers.
1068
            per_layer_attn_metadata = {}
1069
1070
1071
            for attn_group in self.draft_attn_groups:
                for layer_name in attn_group.layer_names:
                    per_layer_attn_metadata[layer_name] = attn_metadata
1072
1073

            # Consider max model length.
1074
1075
1076
            attn_metadata.max_seq_len = min(
                attn_metadata.max_seq_len, self.max_model_len
            )
1077
1078
1079
1080
1081
            # 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.
1082
            block_size = tree_attn_metadata_builder.kv_cache_spec.block_size
1083
            query_positions = flattened_draft_positions[:, level : level + query_len]
1084
            block_numbers = query_positions // block_size
1085
            block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
1086
            slot_mapping = block_ids * block_size + query_positions % block_size
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
            # 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)
1098
            self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
1099

1100
1101
1102
1103
            cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
                num_tokens
            )
            num_input_tokens = batch_desc.num_tokens
1104
            # Run the model.
1105
            with set_forward_context(
1106
1107
1108
1109
                per_layer_attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
1110
1111
1112
                slot_mapping=self._get_slot_mapping(
                    num_input_tokens, attn_metadata.slot_mapping
                ),
1113
            ):
1114
1115
1116
1117
1118
1119
1120
1121
1122
                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(
1123
1124
                batch_size, query_len, -1
            )[:, -level_num_drafts:]
1125
            draft_last_hidden_states = last_hidden_states[:num_tokens].view(
1126
1127
                batch_size, query_len, -1
            )[:, -level_num_drafts:]
1128
1129
1130

            # Get the output logits for the draft tokens.
            logits = self.model.compute_logits(
1131
1132
                draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
            )
1133
1134
1135
1136
1137
1138

            # 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:
1139
1140
1141
                draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                    batch_size, -1
                )
1142
1143
1144
            draft_token_ids_list.append(draft_token_ids)

            # Update the # drafts counters for the next tree level.
1145
            level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
1146
1147
1148
            total_num_drafts = self.cu_drafts_per_level[level + 1]
        return draft_token_ids_list

1149
    def prepare_inputs(
1150
1151
        self,
        common_attn_metadata: CommonAttentionMetadata,
1152
1153
        sampled_token_ids: list[list[int]],
        num_draft_tokens: list[int],
1154
1155
    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
        """
1156
        This function is used to prepare the inputs for speculative decoding.
1157
1158
1159
1160
1161
1162
        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}:
1163
        #       [0, q1, q1 + q2, q1 + q2 + q3]
1164
1165
1166
1167
1168
1169
        #  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}:
1170
        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
1171
        #  common_attn_metadata.seq_lens{_cpu}:
1172
        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
1173
        #  token_indices: [0, 1, ..., q1 - n1 - 1,
1174
1175
        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
1176

1177
1178
1179
1180
        num_rejected_tokens = [
            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
            for i, n in enumerate(num_draft_tokens)
        ]
1181
        num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
1182

1183
1184
        device = common_attn_metadata.query_start_loc.device
        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
1185
        new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
1186
1187

        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
1188
        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
1189
1190
1191
1192
1193
1194
1195
1196
        # [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,
1197
            dtype=torch.int32,
1198
1199
            pin_memory=is_pin_memory_available(),
        )
1200
1201
1202
1203
1204
1205
1206
1207
1208
        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__
1209
1210
1211
        new_query_start_locs_expanded = np.repeat(
            new_query_start_loc_np[:-1], new_num_tokens_per_req_np
        )
1212
1213
1214
        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
        #  _r1_  ____r2____  ___r3__
1215
        token_offsets = (
1216
1217
            self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
        )
1218
1219
1220
1221
1222
1223

        # 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(
1224
1225
            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
        )
1226
        # Final token indices are:
1227
1228
1229
        # [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
1230
        token_indices_np = token_offsets + old_query_start_locs_expanded
1231
        token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
1232
1233

        spec_common_attn_metadata = CommonAttentionMetadata(
1234
            query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
1235
1236
            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
            query_start_loc_cpu=new_query_start_loc_cpu,
1237
1238
            _seq_lens_cpu=new_seq_lens_cpu,
            _num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
1239
1240
1241
            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
1242
            max_seq_len=new_seq_lens_cpu.max().item(),
1243
1244
            block_table_tensor=common_attn_metadata.block_table_tensor,
            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
1245
            causal=True,
1246
            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
1247
        )
1248
1249

        return spec_common_attn_metadata, token_indices
1250

1251
    def get_model_name(self, model: nn.Module) -> str:
1252
        if hasattr(model, "module"):  # multi-GPU
1253
1254
1255
            model = model.module
        return model.__class__.__name__

1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
    def _create_draft_vllm_config(self) -> VllmConfig:
        """Return a VllmConfig with kernel-level overrides for the proposer.
        Subclasses may override to apply additional config changes.
        """
        spec_cfg = self.speculative_config
        if spec_cfg.moe_backend is not None:
            return replace(
                self.vllm_config,
                kernel_config=replace(
                    self.vllm_config.kernel_config,
                    moe_backend=spec_cfg.moe_backend,
                ),
            )
        return self.vllm_config

1271
1272
1273
1274
1275
1276
1277
    def _get_model(self) -> nn.Module:
        """
        Default method to call get_model(). Can be overridden by subclasses which
        need to customize model loading.
        """
        from vllm.compilation.backends import set_model_tag

1278
        draft_vllm_config = self._create_draft_vllm_config()
1279
1280
        with set_model_tag("eagle_head"):
            model = get_model(
1281
                vllm_config=draft_vllm_config,
1282
                model_config=self.speculative_config.draft_model_config,
1283
                load_config=self.speculative_config.draft_load_config,
1284
1285
1286
            )
        return model

1287
    def load_model(self, target_model: nn.Module) -> None:
1288
        target_attn_layer_names = set(
1289
1290
1291
1292
            get_layers_from_vllm_config(
                self.vllm_config,
                AttentionLayerBase,  # type: ignore[type-abstract]
            ).keys()
1293
        )
1294

1295
        self.model = self._get_model()
1296

1297
1298
1299
1300
        # Find draft layers (attention layers added by draft model)
        all_attn_layers = get_layers_from_vllm_config(
            self.vllm_config,
            AttentionLayerBase,  # type: ignore[type-abstract]
1301
        )
1302
1303
        self._draft_attn_layer_names = (
            set(all_attn_layers.keys()) - target_attn_layer_names
1304
        )
1305

1306
        if self.supports_mm_inputs:
1307
1308
1309
            # Even if the target model is multimodal, we can also use
            # text-only draft models
            try:
1310
                dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
1311
                self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
1312
1313
1314
            except (NotImplementedError, AttributeError, TypeError):
                logger.warning(
                    "Draft model does not support multimodal inputs, "
1315
1316
                    "falling back to text-only mode"
                )
1317
                self.supports_mm_inputs = False
1318

1319
1320
        if supports_multimodal(target_model):
            # handle multimodality
1321
            assert hasattr(target_model, "config")
1322
1323
1324
            if self.get_model_name(target_model) in [
                "Qwen2_5_VLForConditionalGeneration",
                "Qwen3VLForConditionalGeneration",
1325
                "Qwen3VLMoeForConditionalGeneration",
1326
                "HunYuanVLForConditionalGeneration",
1327
                "GlmOcrForConditionalGeneration",
1328
1329
                "Qwen3_5ForConditionalGeneration",
                "Qwen3_5MoeForConditionalGeneration",
1330
            ]:
1331
                self.model.config.image_token_index = target_model.config.image_token_id
1332
1333
1334
1335
            elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":
                self.model.config.image_token_index = (
                    target_model.config.vision_config.image_token_id
                )
1336
1337
1338
1339
            elif self.get_model_name(target_model) == "KimiK25ForConditionalGeneration":
                self.model.config.image_token_index = (
                    target_model.config.media_placeholder_token_id
                )
1340
1341
            else:
                self.model.config.image_token_index = (
1342
1343
                    target_model.config.image_token_index
                )
1344
1345
1346
            target_language_model = cast(
                SupportsMultiModal, target_model
            ).get_language_model()
1347
1348
        else:
            target_language_model = target_model
1349

1350
1351
1352
        self._maybe_share_embeddings(target_language_model)
        self._maybe_share_lm_head(target_language_model)

1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
        if (
            self.parallel_drafting
            and self.pass_hidden_states_to_model
            and self.parallel_drafting_hidden_state_tensor is not None
        ):
            flat_mask = self.model.mask_hidden.view(-1)
            if self.eagle3_use_aux_hidden_state:
                # EAGLE3: mask_hidden stores all aux hidden states,
                # project through combine_hidden_states
                self.parallel_drafting_hidden_state_tensor.copy_(
                    self.model.combine_hidden_states(flat_mask)
1364
                )
1365
1366
            else:
                self.parallel_drafting_hidden_state_tensor.copy_(flat_mask)
1367
1368
1369
1370
1371
1372
1373
1374

    def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
        """
        Some draft models may not have their own embedding layers, and some may
        have a duplicate copy of the target model's embedding layers. In these cases,
        we share the target model's embedding layers with the draft model to save
        memory.
        """
1375
        if get_pp_group().world_size == 1:
1376
1377
1378
1379
1380
1381
1382
            inner_model = getattr(target_language_model, "model", None)
            if inner_model is None:
                raise AttributeError("Target model does not have 'model' attribute")
            if hasattr(inner_model, "embed_tokens"):
                target_embed_tokens = inner_model.embed_tokens
            elif hasattr(inner_model, "embedding"):
                target_embed_tokens = inner_model.embedding
1383
1384
            else:
                raise AttributeError(
1385
1386
                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
                )
1387

1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
            share_embeddings = False
            if hasattr(self.model, "has_own_embed_tokens"):
                # EAGLE model
                if not self.model.has_own_embed_tokens:
                    share_embeddings = True
                    logger.info(
                        "Detected EAGLE model without its own embed_tokens in the"
                        " checkpoint. Sharing target model embedding weights with the"
                        " draft model."
                    )
                elif (
                    isinstance(target_embed_tokens.weight, torch.Tensor)
                    and isinstance(self.model.model.embed_tokens.weight, torch.Tensor)
1401
1402
1403
                    # TODO: Offload to CPU for comparison to avoid extra GPU memory
                    # usage in CI testing environments with limited GPU memory
                    and torch.equal(
1404
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                        target_embed_tokens.weight.cpu(),
                        self.model.model.embed_tokens.weight.cpu(),
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                    )
                ):
                    share_embeddings = True
                    logger.info(
                        "Detected EAGLE model with embed_tokens identical to the target"
                        " model. Sharing target model embedding weights with the draft"
                        " model."
                    )
                else:
                    logger.info(
                        "Detected EAGLE model with distinct embed_tokens weights. "
                        "Keeping separate embedding weights from the target model."
                    )
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            else:
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                # MTP model
                share_embeddings = True
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                logger.info(
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                    "Detected MTP model. "
                    "Sharing target model embedding weights with the draft model."
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                )
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            if share_embeddings:
                if hasattr(self.model.model, "embed_tokens"):
                    del self.model.model.embed_tokens
                self.model.model.embed_tokens = target_embed_tokens
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        else:
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            logger.info(
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                "The draft model's vocab embedding will be loaded separately"
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                " from the target model."
            )
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    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
        """
        Some draft models may not have their own LM head, and some may have a
        duplicate copy of the target model's LM head. In these cases, we share
        the target model's LM head with the draft model to save memory.
        """
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        share_lm_head = False
        if hasattr(self.model, "has_own_lm_head"):
            # EAGLE model
            if not self.model.has_own_lm_head:
                share_lm_head = True
                logger.info(
                    "Detected EAGLE model without its own lm_head in the checkpoint. "
                    "Sharing target model lm_head weights with the draft model."
                )
            elif (
                hasattr(target_language_model, "lm_head")
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                and hasattr(target_language_model.lm_head, "weight")
                and hasattr(self.model.lm_head, "weight")
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                and isinstance(target_language_model.lm_head.weight, torch.Tensor)
                and isinstance(self.model.lm_head.weight, torch.Tensor)
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                # TODO: Offload to CPU for comparison to avoid extra GPU memory
                # usage in CI testing environments with limited GPU memory
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                and torch.equal(
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                    target_language_model.lm_head.weight.cpu(),
                    self.model.lm_head.weight.cpu(),
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                )
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            ):
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                share_lm_head = True
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                logger.info(
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                    "Detected EAGLE model with lm_head identical to the target model. "
                    "Sharing target model lm_head weights with the draft model."
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                )
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            else:
                logger.info(
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                    "Detected EAGLE model with distinct lm_head weights. "
                    "Keeping separate lm_head weights from the target model."
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                )
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        else:
            # MTP model
            share_lm_head = True
            logger.info(
                "Detected MTP model. "
                "Sharing target model lm_head weights with the draft model."
            )

        if share_lm_head and hasattr(target_language_model, "lm_head"):
            if hasattr(self.model, "lm_head"):
                del self.model.lm_head
            self.model.lm_head = target_language_model.lm_head
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            # MTP models call compute_logits via shared_head.head (a
            # ParallelLMHead inside each MTP layer), not self.model.lm_head.
            # If the checkpoint omits a copy of the lm_head weights at the
            # MTP layer path, shared_head.head stays uninitialised and
            # produces NaN logits. Always share it explicitly.
            inner = getattr(self.model, "model", None)
            layers = getattr(inner, "layers", None) if inner else None
            if layers is not None:
                items = layers.values() if isinstance(layers, nn.ModuleDict) else layers
                for layer in items:
                    sh = getattr(layer, "shared_head", None)
                    if sh is not None and hasattr(sh, "head"):
                        del sh.head
                        sh.head = target_language_model.lm_head
                        logger.info(
                            "Shared target model lm_head with MTP shared_head.head."
                        )

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        if self.use_local_argmax_reduction:
            if not hasattr(self.model, "get_top_tokens"):
                raise ValueError(
                    "use_local_argmax_reduction is enabled but draft model "
                    f"{self.model.__class__.__name__} does not implement "
                    "get_top_tokens()."
                )
            # Warn if draft model has vocab remapping, which forces fallback
            # to the full-logits path (negating the optimization).
            if (
                hasattr(self.model, "draft_id_to_target_id")
                and self.model.draft_id_to_target_id is not None
            ):
                logger.warning(
                    "use_local_argmax_reduction is enabled but draft model "
                    "uses draft_id_to_target_id vocab remapping. The "
                    "optimization will be bypassed (falling back to full "
                    "logits gather + argmax)."
                )
            else:
                logger.info(
                    "Using local argmax reduction for draft token generation "
                    "(communication: O(2*tp_size) vs O(vocab_size))."
                )

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    @torch.inference_mode()
    def dummy_run(
        self,
        num_tokens: int,
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        use_cudagraphs: bool = True,
        is_graph_capturing: bool = False,
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        slot_mappings: dict[str, torch.Tensor] | None = None,
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    ) -> None:
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        # FIXME: when using tree-based specdec, adjust number of forward-passes
        # according to the depth of the tree.
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        only_one_forward_pass = is_graph_capturing or self.parallel_drafting
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        for fwd_idx in range(
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            1 if only_one_forward_pass else self.num_speculative_tokens
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        ):
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            if fwd_idx <= 1:
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                cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
                    self._determine_batch_execution_and_padding(
                        num_tokens, use_cudagraphs=use_cudagraphs
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                    )
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                )
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            # Make sure to use EAGLE's own buffer during cudagraph capture.
            if (
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                self._draft_attn_layer_names
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                and slot_mappings is not None
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                and next(iter(self._draft_attn_layer_names)) in slot_mappings
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            ):
                slot_mapping_dict = self._get_slot_mapping(num_input_tokens)
            else:
                slot_mapping_dict = slot_mappings or {}

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            with set_forward_context(
                None,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
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                cudagraph_runtime_mode=cudagraph_runtime_mode,
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                slot_mapping=slot_mapping_dict,
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            ):
                if self.supports_mm_inputs:
                    input_ids = None
                    inputs_embeds = self.inputs_embeds[:num_input_tokens]
                else:
                    input_ids = self.input_ids[:num_input_tokens]
                    inputs_embeds = None

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                kwargs = dict(
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                    input_ids=input_ids,
                    positions=self._get_positions(num_input_tokens),
                    inputs_embeds=inputs_embeds,
                )
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                if self.pass_hidden_states_to_model:
                    kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
                self.model(**kwargs)
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    def _get_eagle3_use_aux_hidden_state_from_config(self) -> bool:
        """
        Some eagle3 heads (e.g., nvidia/gpt-oss-120b-Eagle3-v2) do not use auxiliary
        hidden states and directly uses the last layer output just like eagle1.
        They might indicate this by setting "use_aux_hidden_state" to False
        inside the "eagle_config" dict of their hf_config.
        """
        if self.method != "eagle3":
            return False
        # Assume that eagle3 heads use aux hidden states by default
        use_aux_hidden_state = True
        eagle_config = getattr(self.draft_model_config.hf_config, "eagle_config", None)
        if eagle_config is not None:
            use_aux_hidden_state = eagle_config.get("use_aux_hidden_state", True)
        return use_aux_hidden_state

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    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
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        """
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        Validate that all drafting layers belong to the same KVCacheGroup.
        Need this assumption to ensure all drafting layers can use the
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        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]
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                        for layer_name in self._draft_attn_layer_names
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                    ]
                )
            )
            == 1
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        ), "All drafting layers should belong to the same kv cache group"
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    def initialize_attn_backend(
        self,
        kv_cache_config: KVCacheConfig,
        kernel_block_sizes: list[int] | None = None,
    ) -> None:
        """
        Initialize AttentionGroups for draft layers using kv_cache_config.
        Called from the model runner's initialize_metadata_builders.
        """
        all_attn_layers = get_layers_from_vllm_config(
            self.vllm_config,
            AttentionLayerBase,  # type: ignore[type-abstract]
        )

        # Find which kv_cache_group the draft layers belong to
        self.validate_same_kv_cache_group(kv_cache_config)
        kv_cache_spec = None
        for gid, group in enumerate(kv_cache_config.kv_cache_groups):
            if self._draft_attn_layer_names & set(group.layer_names):
                self.kv_cache_gid = gid
                kv_cache_spec = group.kv_cache_spec
                break

        attention_groups: dict[tuple[str, str], AttentionGroup] = {}
        if kv_cache_spec is not None:
            for layer_name in self._draft_attn_layer_names:
                attn_backend = all_attn_layers[layer_name].get_attn_backend()
                backend_key = attn_backend.full_cls_name()
                if backend_key not in attention_groups:
                    layer_kv_cache_spec = kv_cache_spec
                    if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
                        layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[
                            layer_name
                        ]

                    kernel_block_size = (
                        kernel_block_sizes[self.kv_cache_gid]
                        if kernel_block_sizes is not None
                        and self.kv_cache_gid < len(kernel_block_sizes)
                        else None
                    )
                    attn_group = AttentionGroup(
                        backend=attn_backend,
                        layer_names=[layer_name],
                        kv_cache_spec=layer_kv_cache_spec,
                        kv_cache_group_id=self.kv_cache_gid,
                    )
                    attn_group.create_metadata_builders(
                        self.vllm_config,
                        self.device,
                        kernel_block_size=kernel_block_size,
                    )
                    attention_groups[backend_key] = attn_group
                else:
                    attention_groups[backend_key].layer_names.append(layer_name)

        self.draft_attn_groups = list(attention_groups.values())
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        self.block_size = (
            self.draft_attn_groups[0].get_metadata_builder().kv_cache_spec.block_size
        )
        logger.debug("Using block size %d for drafting layers", self.block_size)
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    def _determine_batch_execution_and_padding(
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        self,
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        num_tokens: int,
        use_cudagraphs: bool = True,
    ) -> tuple[CUDAGraphMode, int, torch.Tensor | None]:
        cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
            num_tokens,
            valid_modes=({CUDAGraphMode.NONE} if not use_cudagraphs else None),
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        )
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        num_tokens_padded = batch_desc.num_tokens

        # Extra coordination when running data-parallel since we need to
        # coordinate across ranks
        # TODO(Flechman): support DBO ubatching
        should_ubatch, num_tokens_across_dp = False, None
        if self.vllm_config.parallel_config.data_parallel_size > 1:
            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
                coordinate_batch_across_dp(
                    num_tokens_unpadded=num_tokens,
                    parallel_config=self.vllm_config.parallel_config,
                    allow_microbatching=False,
                    num_tokens_padded=num_tokens_padded,
                    cudagraph_mode=cudagraph_mode.value,
                )
            )
            assert not should_ubatch, "DBO ubatching not implemented for EAGLE"

            # Extract DP-synced values
            if num_tokens_across_dp is not None:
                dp_rank = self.dp_rank
                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
                # Re-dispatch with DP padding so we have the correct
                # batch_descriptor
                cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
                    num_tokens_padded,
                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
                )
                # Assert to make sure the agreed upon token count is correct
                # otherwise num_tokens_across_dp will no-longer be valid
                assert batch_desc.num_tokens == num_tokens_padded
                num_tokens_across_dp[dp_rank] = num_tokens_padded
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        return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
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class EagleProposer(SpecDecodeBaseProposer):
    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
        runner=None,
    ):
        super().__init__(
            vllm_config,
            device,
            pass_hidden_states_to_model=True,
            runner=runner,
        )


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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

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    assert sampling_metadata.temperature is not None

    # Use epsilon comparison to detect greedy sampling (temperature ~ 0.0)
    # consistent with sampler.py's _SAMPLING_EPS threshold
    temperature = sampling_metadata.temperature
    # Avoid division by zero if there are greedy requests.
    if not sampling_metadata.all_random:
        is_greedy = temperature < _SAMPLING_EPS
        temperature = torch.where(is_greedy, 1.0, temperature)
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    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)
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        next_token_ids = torch.where(is_greedy, greedy_token_ids, next_token_ids)
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    return next_token_ids, probs