eagle.py 52.7 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import ast
from dataclasses import replace
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from importlib.util import find_spec
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import numpy as np
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import torch
import torch.nn as nn

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

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PADDING_SLOT_ID = -1

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class EagleProposer:
    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
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        runner=None,
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    ):
        self.vllm_config = vllm_config
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        self.speculative_config = vllm_config.speculative_config
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        assert self.speculative_config is not None
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        self.draft_model_config = self.speculative_config.draft_model_config
        self.method = self.speculative_config.method
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        self.runner = runner
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        self.device = device
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        self.dtype = vllm_config.model_config.dtype
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        self.max_model_len = vllm_config.model_config.max_model_len
        self.block_size = vllm_config.cache_config.block_size
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        self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
        self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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        self.token_arange_np = np.arange(self.max_num_tokens)
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        # We need to get the hidden size from the draft model config because
        # the draft model's hidden size can be different from the target model's
        # hidden size (e.g., Llama 3.3 70B).
        self.hidden_size = self.draft_model_config.get_hidden_size()
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        # Multi-modal data support
        self.mm_registry = MULTIMODAL_REGISTRY
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            vllm_config.model_config
        )
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        self.attn_metadata_builder: AttentionMetadataBuilder | None = None
        self.draft_indexer_metadata_builder: AttentionMetadataBuilder | None = None
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        self.attn_layer_names: list[str] = []
        self.indexer_layer_names: list[str] = []
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        self.use_cuda_graph = False

        compilation_config = self.vllm_config.compilation_config
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        if compilation_config.mode == CompilationMode.VLLM_COMPILE:
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            cudagraph_mode = compilation_config.cudagraph_mode
            if cudagraph_mode != CUDAGraphMode.NONE and not cudagraph_mode.has_mode(
                CUDAGraphMode.PIECEWISE
            ):
                logger.warning(
                    "Currently the eagle proposer only supports cudagraph_mode "
                    "PIECEWISE, if you want the drafter to use cuda graphs, "
                    "please set compilation_config.cudagraph_mode to PIECEWISE "
                    "or FULL_AND_PIECEWISE"
                )
            self.use_cuda_graph = (
                cudagraph_mode.has_mode(CUDAGraphMode.PIECEWISE)
                and not self.speculative_config.enforce_eager
            )

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        self.cudagraph_batch_sizes = (
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            (sorted(self.vllm_config.compilation_config.cudagraph_capture_sizes))
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            if self.use_cuda_graph
            else []
        )
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        self.use_cuda_graph = self.use_cuda_graph and bool(self.cudagraph_batch_sizes)
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        # persistent buffers for cuda graph
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        self.input_ids = torch.zeros(
            self.max_num_tokens, dtype=torch.int32, device=device
        )
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        self.uses_mrope = self.vllm_config.model_config.uses_mrope
        if self.uses_mrope:
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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_num_tokens + 1), dtype=torch.int64, device=device
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            )
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        else:
            # RoPE need (max_num_tokens,)
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            self.positions = torch.zeros(
                self.max_num_tokens, dtype=torch.int64, device=device
            )
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        self.hidden_states = torch.zeros(
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            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
        )
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        # We need +1 here because the arange is used to set query_start_loc,
        # which has one more element than batch_size.
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        max_batch_size = vllm_config.scheduler_config.max_num_seqs
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        max_num_slots_for_arange = max(max_batch_size + 1, self.max_num_tokens)
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        self.arange = torch.arange(
            max_num_slots_for_arange, device=device, dtype=torch.int32
        )
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        self.inputs_embeds = torch.zeros(
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            (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
        )
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        self.backup_next_token_ids = CpuGpuBuffer(
            max_batch_size,
            dtype=torch.int32,
            pin_memory=is_pin_memory_available(),
            device=device,
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            with_numpy=True,
        )
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        # Determine allowed attention backends once during initialization.
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        from vllm.attention.backends.registry import AttentionBackendEnum

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        self.allowed_attn_types: tuple | None = None
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        if current_platform.is_rocm():
            rocm_types = [TritonAttentionMetadata, FlashAttentionMetadata]
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            # ROCM_AITER_FA is an optional backend
            if find_spec(
                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)
            self.allowed_attn_types = tuple(rocm_types)

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

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

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

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    def propose(
        self,
        # [num_tokens]
        target_token_ids: torch.Tensor,
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        # [num_tokens] or [3, num_tokens] when M-RoPE is enabled
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        target_positions: torch.Tensor,
        # [num_tokens, hidden_size]
        target_hidden_states: torch.Tensor,
        # [batch_size]
        next_token_ids: torch.Tensor,
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        last_token_indices: 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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    ) -> torch.Tensor:
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        num_tokens = target_token_ids.shape[0]
        batch_size = next_token_ids.shape[0]
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        if last_token_indices is None:
            last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
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        if self.method == "eagle3":
            assert isinstance(self.model, Eagle3LlamaForCausalLM)
            target_hidden_states = self.model.combine_hidden_states(
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                target_hidden_states
            )
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            assert target_hidden_states.shape[-1] == self.hidden_size
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        # Shift the input ids by one token.
        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
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        self.input_ids[: num_tokens - 1] = target_token_ids[1:]
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        # Replace the last token with the next token.
        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
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        self.input_ids[last_token_indices] = next_token_ids
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        assert self.runner is not None
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        if self.attn_metadata_builder is None:
            attn_metadata_builder = self._get_attention_metadata_builder()
        else:
            attn_metadata_builder = self.attn_metadata_builder

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        attn_metadata = attn_metadata_builder.build_for_drafting(
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            common_attn_metadata=common_attn_metadata, draft_index=0
        )
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        # FIXME: support hybrid kv for draft model (remove separate indexer)
        if self.draft_indexer_metadata_builder:
            draft_indexer_metadata = (
                self.draft_indexer_metadata_builder.build_for_drafting(
                    common_attn_metadata=common_attn_metadata,
                    draft_index=0,
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                )
            )
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        else:
            draft_indexer_metadata = None
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        # At this moment, we assume all eagle layers belong to the same KV
        # cache group, thus using the same attention metadata.
        per_layer_attn_metadata = {}
        for layer_name in self.attn_layer_names:
            per_layer_attn_metadata[layer_name] = attn_metadata
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        for layer_name in self.indexer_layer_names:
            assert draft_indexer_metadata is not None
            per_layer_attn_metadata[layer_name] = draft_indexer_metadata

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

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

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

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

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        if self.use_cuda_graph and batch_size <= self.cudagraph_batch_sizes[-1]:
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            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
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            cudagraph_runtime_mode = CUDAGraphMode.PIECEWISE
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        else:
            input_batch_size = batch_size
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            cudagraph_runtime_mode = CUDAGraphMode.NONE
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        common_attn_metadata.num_actual_tokens = batch_size
        common_attn_metadata.max_query_len = 1
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        common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
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        common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
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            self.token_arange_np[: batch_size + 1]
        ).clone()
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        for token_index in range(self.num_speculative_tokens - 1):
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            # Update the inputs.
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            # cast to int32 is crucial when eagle model is compiled.
            # tensor.argmax() returns int64 by default.
            input_ids = draft_token_ids_list[-1].int()
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            if self.uses_mrope:
                positions += 1
                # NOTE(woosuk): We should handle the case where the draft model
                # generates tokens beyond the max model length.
                # Since it is complex to remove such requests from the batch,
                # we keep them in the batch but adjust the position ids
                # and slot mappings to avoid the
                # out-of-range access during the model execution.
                # The draft tokens generated with this adjustment
                # should be ignored.
                exceeds_max_model_len = positions[0] >= self.max_model_len
                # Mask out the position ids that exceed the max model length.
                # Otherwise, we may get out-of-range error in RoPE.
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                clamped_positions = torch.where(
                    exceeds_max_model_len.unsqueeze(0),
                    torch.zeros_like(positions),
                    positions,
                )
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            else:
                positions += 1
                exceeds_max_model_len = positions >= self.max_model_len
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                clamped_positions = torch.where(exceeds_max_model_len, 0, positions)
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            # For data integrity when async scheduling, we shouldn't use in place
            # operations in case they are modified in next step's `prepare_input`
            # of main model.
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            # Increment the sequence lengths.
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            common_attn_metadata.seq_lens += 1
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            # This is an out-of-place operation to avoid modifying the original tensor.
            common_attn_metadata.seq_lens_cpu = common_attn_metadata.seq_lens_cpu + 1
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            # For the requests that exceed the max model length, we set the
            # sequence length to 1 to minimize their overheads in attention.
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            common_attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
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            common_attn_metadata.num_computed_tokens_cpu = (
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                common_attn_metadata.seq_lens_cpu - 1
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            )
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            # Compute the slot mapping.
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            if self.uses_mrope:
                # all dimensions of positions are the same
                block_numbers = clamped_positions[0] // self.block_size
            else:
                block_numbers = clamped_positions // self.block_size
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            block_ids = common_attn_metadata.block_table_tensor.gather(
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                dim=1, index=block_numbers.view(-1, 1)
            )
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            block_ids = block_ids.view(-1)
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            if self.uses_mrope:
                common_attn_metadata.slot_mapping = (
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                    block_ids * self.block_size + clamped_positions[0] % self.block_size
                )
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            else:
                common_attn_metadata.slot_mapping = (
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                    block_ids * self.block_size + clamped_positions % self.block_size
                )
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            # Mask out the slot mappings that exceed the max model length.
            # Otherwise, the KV cache will be inadvertently updated with the
            # padding tokens.
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            common_attn_metadata.slot_mapping.masked_fill_(
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                exceeds_max_model_len, PADDING_SLOT_ID
            )
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            # Rebuild attention metadata
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            attn_metadata = attn_metadata_builder.build_for_drafting(  # type: ignore
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                common_attn_metadata=common_attn_metadata, draft_index=token_index + 1
            )
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            for layer_name in self.attn_layer_names:
                per_layer_attn_metadata[layer_name] = attn_metadata
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            # copy inputs to buffer for cudagraph
            self.input_ids[:batch_size] = input_ids
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            self._set_positions(batch_size, clamped_positions)
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            self.hidden_states[:batch_size] = hidden_states
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            if self.supports_mm_inputs:
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                self.inputs_embeds[:batch_size] = self.model.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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            with set_forward_context(
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                per_layer_attn_metadata,
                self.vllm_config,
                num_tokens=input_batch_size,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
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            ):
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                ret_hidden_states = self.model(
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                    input_ids=input_ids,
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                    positions=self._get_positions(input_batch_size),
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                    hidden_states=self.hidden_states[:input_batch_size],
                    inputs_embeds=inputs_embeds,
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                )
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                if self.method == "mtp":
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                    last_hidden_states = ret_hidden_states
                    hidden_states = ret_hidden_states
                else:
                    last_hidden_states, hidden_states = ret_hidden_states
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            hidden_states = hidden_states[:batch_size]
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            logits = self.model.compute_logits(last_hidden_states[:batch_size])
490
            draft_token_ids = logits.argmax(dim=-1)
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            draft_token_ids_list.append(draft_token_ids)

        # [batch_size, num_speculative_tokens]
        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
495
        return draft_token_ids
496

497
    def prepare_next_token_ids_cpu(
498
        self,
499
        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):
514
            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]
522
                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)
525
        next_token_ids = torch.tensor(
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            next_token_ids, dtype=torch.int32, device=self.input_ids.device
        )
528
        return next_token_ids
529

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538
    def prepare_next_token_ids_padded(
        self,
        common_attn_metadata: CommonAttentionMetadata,
        sampled_token_ids: torch.Tensor,
        requests: dict[str, CachedRequestState],
        gpu_input_batch: InputBatch,
        discard_request_indices: torch.Tensor,
        num_discarded_requests: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
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551
        """
        This function is used to prepare the inputs for speculative decoding.
        It calculates the next token ids and the number of valid sampled tokens
        for each request, considering the "discarded" requests whose next token
        is not sampled and comes from `request.get_token_id()` instead.
        It also accounts for the rejected tokens in `sampled_token_ids`.
        This function must use device functions to operate on the inputs, and
        should not introduce any blocking CPU-GPU synchronization.
        """
        # TODO(Ben): Combine this into a custom fused kernel

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

        # Mask out the sampled tokens indices that should not be sampled.
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        discard_sampled_tokens_req_indices = discard_request_indices[
            :num_discarded_requests
        ]
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568

        valid_sampled_token_ids_gpu = sampled_token_ids.clone()
        valid_sampled_token_ids_gpu.index_fill_(
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            0, discard_sampled_tokens_req_indices, -1
        )
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572

        # Generate a mask for all valid tokens within those requests
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        valid_mask = (valid_sampled_token_ids_gpu != -1) & (
            valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size
        )
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586

        # Count the number of valid tokens in each request
        valid_sampled_tokens_count = valid_mask.sum(dim=1)

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

        # Get last valid token from each row
        # (assume undefined state where there is no valid token)
        selected_tokens = torch.gather(
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            valid_sampled_token_ids_gpu, 1, last_valid_indices_safe.unsqueeze(1)
        ).squeeze(1)
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592

        # Use last token if valid, pre-computed backup if not
        batch_size = valid_sampled_token_ids_gpu.shape[0]
        next_token_ids = torch.where(
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            last_valid_indices != -1,
            selected_tokens,
            self.backup_next_token_ids.gpu[:batch_size],
        )
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        return next_token_ids, valid_sampled_tokens_count

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    def prepare_inputs_padded(
        self,
        common_attn_metadata: CommonAttentionMetadata,
        spec_decode_metadata: SpecDecodeMetadata,
        valid_sampled_tokens_count: torch.Tensor,
    ) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
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        """
        This function is used to prepare the inputs for speculative decoding
        It updates the common_attn_metadata for speculative decoding,
        but does not consider the rejected tokens. Instead, all tokens
        are included as inputs to the speculator, with the rejected tokens
        used as padding and filtered out later by `token_indices_to_sample`.
        No blocking CPU operations should be introduced in this function.
        """
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619
620
        num_draft_tokens_gpu = torch.cat(
            [
                spec_decode_metadata.cu_num_draft_tokens[0:1],
                spec_decode_metadata.cu_num_draft_tokens[1:]
                - spec_decode_metadata.cu_num_draft_tokens[:-1],
            ]
        )
621
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623
624

        num_rejected_tokens_gpu = torch.where(
            num_draft_tokens_gpu > 0,
            num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
625
626
            torch.zeros_like(num_draft_tokens_gpu),
        )
627
628
629

        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu

630
        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
631
632
633
634
635
636
637
638
639

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

        spec_common_attn_metadata = CommonAttentionMetadata(
            query_start_loc=common_attn_metadata.query_start_loc,
            seq_lens=common_attn_metadata.seq_lens,
            query_start_loc_cpu=query_start_loc_cpu,
            seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
640
            num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
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647
            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
            max_seq_len=common_attn_metadata.seq_lens_cpu.max().item(),
            block_table_tensor=common_attn_metadata.block_table_tensor,
            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
            causal=True,
648
            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
649
650
        )

651
652
653
        token_indices_to_sample = (
            common_attn_metadata.query_start_loc[1:] - 1 - num_rejected_tokens_gpu
        )
654
655
656

        return spec_common_attn_metadata, token_indices, token_indices_to_sample

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664
665
666
667
    def propose_tree(
        self,
        batch_size: int,
        # [num_tokens, vocab_size]
        logits: torch.Tensor,
        # [num_tokens]
        positions: torch.Tensor,
        # [num_tokens, hidden_size]
        hidden_states: torch.Tensor,
        common_attn_metadata: CommonAttentionMetadata,
    ) -> list[torch.Tensor]:
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671
        tree_attn_metadata_builder = self.runner.attn_groups[0][
            0
        ].get_metadata_builder()
        assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
672

673
        total_num_drafts = self.cu_drafts_per_level[0]
674
675
        level_num_drafts = total_num_drafts
        # Sample a draft token for each child at the tree root level.
676
        num_children = self.child_drafts_per_level[0]
677
678
679
        if num_children == 1:
            draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
        else:
680
681
682
            draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                batch_size, -1
            )
683
684
685
686
        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.
687
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689
690
691
692
693
694
695
        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
        )
696
697
        # Precompute the draft token positions.
        flattened_draft_positions = (
698
699
            positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
        )
700
        tree_depth = len(self.cu_drafts_per_level)
701
        for level in range(tree_depth - 1):
702
703
            # Get draft positions for RoPE.
            draft_positions = positions + (level + 1)
704
            exceeds_max_model_len = (positions + total_num_drafts) >= self.max_model_len
705
706
            # Mask out the position ids that exceed the max model length.
            # Otherwise, we may get out-of-range error in RoPE.
707
            draft_positions = torch.where(
708
709
710
                exceeds_max_model_len,
                0,
                draft_positions,
711
712
            ).view(batch_size, -1)

713
714
            if level_num_drafts > 1:
                # Repeat the positions for each draft at this level.
715
                draft_positions = draft_positions.repeat_interleave(
716
717
                    level_num_drafts, dim=1
                )
718
719
720
721

            if num_children > 1:
                # Repeat draft hidden states for each child.
                draft_hidden_states = draft_hidden_states.repeat_interleave(
722
723
                    num_children, dim=1
                )
724
725

            # Concatenate the draft tokens, positions, and hidden states.
726
727
            tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
            tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
728
            tree_hidden_states = torch.cat(
729
730
                [tree_hidden_states, draft_hidden_states], dim=1
            )
731
732
733

            # Build new attention metadata for the next level of drafts.
            # This is necessary to support tree attention.
734
            query_len = total_num_drafts
735
736
            common_attn_metadata = replace(
                common_attn_metadata,
737
                query_start_loc=query_len * self.arange[: batch_size + 1],
738
739
740
741
742
743
                seq_lens=common_attn_metadata.seq_lens + level_num_drafts,
                num_actual_tokens=batch_size * query_len,
                max_query_len=query_len,
            )
            attn_metadata = tree_attn_metadata_builder.build_for_drafting(
                common_attn_metadata=common_attn_metadata,
744
                draft_index=level + 1,
745
746
747
748
749
750
751
752
            )

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

            # Consider max model length.
753
754
755
            attn_metadata.max_seq_len = min(
                attn_metadata.max_seq_len, self.max_model_len
            )
756
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758
759
760
            # 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.
761
            query_positions = flattened_draft_positions[:, level : level + query_len]
762
            block_numbers = query_positions // self.block_size
763
764
765
766
            block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
            slot_mapping = (
                block_ids * self.block_size + query_positions % self.block_size
            )
767
768
769
770
771
772
773
774
775
776
777
            # 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)
778
            self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
779

780
781
            if self.use_cuda_graph and num_tokens <= self.cudagraph_batch_sizes[-1]:
                num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
782
                cudagraph_runtime_mode = CUDAGraphMode.PIECEWISE
783
784
            else:
                num_input_tokens = num_tokens
785
                cudagraph_runtime_mode = CUDAGraphMode.NONE
786
            # Run the model.
787
            with set_forward_context(
788
789
790
791
                per_layer_attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
792
            ):
793
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795
796
797
798
799
800
801
                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(
802
803
                batch_size, query_len, -1
            )[:, -level_num_drafts:]
804
            draft_last_hidden_states = last_hidden_states[:num_tokens].view(
805
806
                batch_size, query_len, -1
            )[:, -level_num_drafts:]
807
808
809

            # Get the output logits for the draft tokens.
            logits = self.model.compute_logits(
810
811
                draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
            )
812
813
814
815
816
817

            # 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:
818
819
820
                draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
                    batch_size, -1
                )
821
822
823
            draft_token_ids_list.append(draft_token_ids)

            # Update the # drafts counters for the next tree level.
824
            level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
825
826
827
            total_num_drafts = self.cu_drafts_per_level[level + 1]
        return draft_token_ids_list

828
    def prepare_inputs(
829
830
        self,
        common_attn_metadata: CommonAttentionMetadata,
831
832
        sampled_token_ids: list[list[int]],
        num_draft_tokens: list[int],
833
834
    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
        """
835
        This function is used to prepare the inputs for speculative decoding.
836
837
838
839
840
841
        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}:
842
        #       [0, q1, q1 + q2, q1 + q2 + q3]
843
844
845
846
847
848
        #  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}:
849
        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
850
        #  common_attn_metadata.seq_lens{_cpu}:
851
        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
852
        #  token_indices: [0, 1, ..., q1 - n1 - 1,
853
854
        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
855

856
857
858
859
        num_rejected_tokens = [
            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
            for i, n in enumerate(num_draft_tokens)
        ]
860
        num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
861

862
863
        device = common_attn_metadata.query_start_loc.device
        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
864
        new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
865
866

        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
867
        new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
868
869
870
871
872
873
874
875
        # [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,
876
            dtype=torch.int32,
877
878
            pin_memory=is_pin_memory_available(),
        )
879
880
881
882
883
884
885
886
887
        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__
888
889
890
        new_query_start_locs_expanded = np.repeat(
            new_query_start_loc_np[:-1], new_num_tokens_per_req_np
        )
891
892
893
        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
        #  _r1_  ____r2____  ___r3__
894
895
896
        token_offests = (
            self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
        )
897
898
899
900
901
902

        # 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(
903
904
            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
        )
905
        # Final token indices are:
906
907
908
        # [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
909
        token_indices_np = token_offests + old_query_start_locs_expanded
910
        token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
911
912

        spec_common_attn_metadata = CommonAttentionMetadata(
913
            query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
914
915
916
            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
            query_start_loc_cpu=new_query_start_loc_cpu,
            seq_lens_cpu=new_seq_lens_cpu,
917
            num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
918
919
920
            num_reqs=common_attn_metadata.num_reqs,
            num_actual_tokens=total_num_tokens,
            max_query_len=new_query_len_per_req.max().item(),
921
            max_seq_len=new_seq_lens_cpu.max().item(),
922
923
            block_table_tensor=common_attn_metadata.block_table_tensor,
            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
924
            causal=True,
925
            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
926
        )
927
928

        return spec_common_attn_metadata, token_indices
929

930
    def get_model_name(self, model: nn.Module) -> str:
931
        if hasattr(model, "module"):  # multi-GPU
932
933
934
            model = model.module
        return model.__class__.__name__

935
    def load_model(self, target_model: nn.Module) -> None:
936
        draft_model_config = self.vllm_config.speculative_config.draft_model_config
937
        target_attn_layer_names = set(
938
            get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase).keys()
939
        )
940
941
        # FIXME: support hybrid kv for draft model
        target_indexer_layer_names = set(
942
943
944
945
            get_layers_from_vllm_config(
                self.vllm_config, DeepseekV32IndexerCache
            ).keys()
        )
946

947
        from vllm.compilation.backends import set_model_tag
948

949
        with set_model_tag("eagle_head"):
950
951
952
            self.model = get_model(
                vllm_config=self.vllm_config, model_config=draft_model_config
            )
953

954
        draft_attn_layer_names = (
955
            get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase).keys()
956
957
958
959
960
961
            - target_attn_layer_names
        )
        indexer_layers = get_layers_from_vllm_config(
            self.vllm_config, DeepseekV32IndexerCache
        )
        draft_indexer_layer_names = indexer_layers.keys() - target_indexer_layer_names
962
        self.attn_layer_names = list(draft_attn_layer_names - draft_indexer_layer_names)
963
964
965
966
967
        self.indexer_layer_names = list(draft_indexer_layer_names)

        if self.indexer_layer_names:
            first_layer = self.indexer_layer_names[0]
            self.draft_indexer_metadata_builder = (
968
969
970
                indexer_layers[first_layer]
                .get_attn_backend()
                .get_builder_cls()(
971
                    indexer_layers[first_layer].get_kv_cache_spec(self.vllm_config),
972
973
974
                    self.indexer_layer_names,
                    self.vllm_config,
                    self.device,
975
976
                )
            )
977
978
        else:
            self.draft_indexer_metadata_builder = None
979

980
        if self.supports_mm_inputs:
981
982
983
            # Even if the target model is multimodal, we can also use
            # text-only draft models
            try:
984
                dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
985
                self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
986
987
988
            except (NotImplementedError, AttributeError, TypeError):
                logger.warning(
                    "Draft model does not support multimodal inputs, "
989
990
                    "falling back to text-only mode"
                )
991
                self.supports_mm_inputs = False
992

993
994
        if supports_multimodal(target_model):
            # handle multimodality
995
996
997
998
999
            if (
                self.get_model_name(target_model)
                == "Qwen2_5_VLForConditionalGeneration"
            ):
                self.model.config.image_token_index = target_model.config.image_token_id
1000
1001
            else:
                self.model.config.image_token_index = (
1002
1003
                    target_model.config.image_token_index
                )
1004
1005
1006
            target_language_model = target_model.get_language_model()
        else:
            target_language_model = target_model
1007

1008
        # share embed_tokens with the target model if needed
1009
        if get_pp_group().world_size == 1:
1010
            if hasattr(target_language_model.model, "embed_tokens"):
1011
                target_embed_tokens = target_language_model.model.embed_tokens
1012
            elif hasattr(target_language_model.model, "embedding"):
1013
1014
1015
                target_embed_tokens = target_language_model.model.embedding
            else:
                raise AttributeError(
1016
1017
                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
                )
1018

1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
            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)
1032
1033
1034
1035
1036
                    and torch.allclose(
                        target_embed_tokens.weight.cpu(),
                        self.model.model.embed_tokens.weight.cpu(),
                        rtol=1e-5,
                        atol=1e-7,
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
                    )
                ):
                    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."
                    )
1050
            else:
1051
1052
                # MTP model
                share_embeddings = True
1053
                logger.info(
1054
1055
                    "Detected MTP model. "
                    "Sharing target model embedding weights with the draft model."
1056
                )
1057
1058
1059
1060
1061

            if share_embeddings:
                if hasattr(self.model.model, "embed_tokens"):
                    del self.model.model.embed_tokens
                self.model.model.embed_tokens = target_embed_tokens
1062
        else:
1063
            logger.info(
1064
                "The draft model's vocab embedding will be loaded separately"
1065
1066
                " from the target model."
            )
1067
1068

        # share lm_head with the target model if needed
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
        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")
                and isinstance(target_language_model.lm_head.weight, torch.Tensor)
                and isinstance(self.model.lm_head.weight, torch.Tensor)
                and torch.equal(
                    target_language_model.lm_head.weight, self.model.lm_head.weight
                )
1085
            ):
1086
                share_lm_head = True
1087
                logger.info(
1088
1089
                    "Detected EAGLE model with lm_head identical to the target model. "
                    "Sharing target model lm_head weights with the draft model."
1090
                )
1091
1092
            else:
                logger.info(
1093
1094
                    "Detected EAGLE model with distinct lm_head weights. "
                    "Keeping separate lm_head weights from the target model."
1095
                )
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
        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
1108

1109
1110
1111
1112
    @torch.inference_mode()
    def dummy_run(
        self,
        num_tokens: int,
1113
        use_cudagraphs=True,
1114
    ) -> None:
1115
1116
1117
        # Determine if CUDA graphs should be used for this run.
        cudagraphs_enabled = use_cudagraphs and self.use_cuda_graph
        if cudagraphs_enabled and num_tokens <= self.cudagraph_batch_sizes[-1]:
1118
1119
1120
1121
1122
1123
            num_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)

        with set_forward_context(
            None,
            self.vllm_config,
            num_tokens=num_tokens,
1124
1125
1126
            cudagraph_runtime_mode=(
                CUDAGraphMode.PIECEWISE if cudagraphs_enabled else CUDAGraphMode.NONE
            ),
1127
        ):
1128
            if self.supports_mm_inputs:
1129
1130
1131
1132
1133
1134
                input_ids = None
                inputs_embeds = self.inputs_embeds[:num_tokens]
            else:
                input_ids = self.input_ids[:num_tokens]
                inputs_embeds = None

1135
            self.model(
1136
                input_ids=input_ids,
1137
                positions=self._get_positions(num_tokens),
1138
1139
                hidden_states=self.hidden_states[:num_tokens],
                inputs_embeds=inputs_embeds,
1140
            )
1141

1142
    def _get_attention_metadata_builder(self) -> AttentionMetadataBuilder:
1143
        """Find and return the attention metadata builders for EAGLE layers.
1144

1145
1146
        Returns:
            The metadata builders for EAGLE layers.
1147

1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
        Raises:
            AssertionError: If no metadata builders are found for EAGLE layers.
        """
        builder = None
        chosen_layer = self.attn_layer_names[0]

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

        assert builder is not None, (
1163
1164
            "Failed to find attention metadata builder for EAGLE layers."
        )
1165
1166
        return builder

1167
    def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
        """
        Validate that all eagle layers belong to the same KVCacheGroup.
        Need this assumption to ensure all eagle layers can use the
        same AttentionMetadata.
        May extend to multiple AttentionMetadata in the future.
        """
        kv_cache_groups: dict[str, int] = {}
        for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
            for layer_name in kv_cache_group.layer_names:
                kv_cache_groups[layer_name] = id
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
        assert (
            len(
                set(
                    [
                        kv_cache_groups[layer_name]
                        for layer_name in self.attn_layer_names
                    ]
                )
            )
            == 1
        ), "All eagle layers should belong to the same kv cache group"
1189

1190

1191
1192
1193
1194
# 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.
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
# 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

1208
1209
1210
1211
1212
1213
1214
1215
1216
    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)
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
    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_()
1228
1229
1230
    # 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)
1231
1232
1233
1234
1235
1236
1237
1238
    if not sampling_metadata.all_random:
        greedy_token_ids = probs.argmax(dim=-1)
        next_token_ids = torch.where(
            is_greedy,
            greedy_token_ids,
            next_token_ids,
        )
    return next_token_ids, probs