Skip to content

GitLab

Unverified Commit cde8d247 authored by Matthew Bonanni's avatar Matthew Bonanni Committed by GitHub
Browse files

[Spec Decode] Move `SpecDecodeBaseProposer` out of `eagle.py` (#40732) 


Signed-off-by: default avatarMatthew Bonanni <mbonanni@redhat.com>
parent 4a6dd1c3
Hide whitespace changes
Inline Side-by-side
Showing with 1792 additions and 1785 deletions
tests/v1/spec_decode/test_eagle.py
View file @ cde8d247
...@@ -741,9 +741,9 @@ def test_set_inputs_first_pass_parallel_drafting(): ...@@ -741,9 +741,9 @@ def test_set_inputs_first_pass_parallel_drafting():
@pytest.mark.parametrize("pp_size", [1, 2]) @pytest.mark.parametrize("pp_size", [1, 2])
@pytest.mark.parametrize("use_distinct_embed_tokens", [True, False]) @pytest.mark.parametrize("use_distinct_embed_tokens", [True, False])
@pytest.mark.parametrize("use_distinct_lm_head", [True, False]) @pytest.mark.parametrize("use_distinct_lm_head", [True, False])
@mock.patch("vllm.v1.spec_decode.eagle.get_pp_group") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_pp_group")
@mock.patch("vllm.v1.spec_decode.eagle.get_layers_from_vllm_config") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_layers_from_vllm_config")
@mock.patch("vllm.v1.spec_decode.eagle.get_model") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_model")
def test_load_model( def test_load_model(
mock_get_model, mock_get_model,
mock_get_layers, mock_get_layers,
......
tests/v1/spec_decode/test_mtp.py
View file @ cde8d247
...@@ -61,9 +61,9 @@ def _create_mtp_proposer(num_speculative_tokens: int) -> EagleProposer: ...@@ -61,9 +61,9 @@ def _create_mtp_proposer(num_speculative_tokens: int) -> EagleProposer:
return EagleProposer(vllm_config=vllm_config, device=DEVICE_TYPE) return EagleProposer(vllm_config=vllm_config, device=DEVICE_TYPE)
@mock.patch("vllm.v1.spec_decode.eagle.get_pp_group") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_pp_group")
@mock.patch("vllm.v1.spec_decode.eagle.get_layers_from_vllm_config") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_layers_from_vllm_config")
@mock.patch("vllm.v1.spec_decode.eagle.get_model") @mock.patch("vllm.v1.spec_decode.llm_base_proposer.get_model")
def test_mtp_load_model_unified(mock_get_model, mock_get_layers, mock_get_pp_group): def test_mtp_load_model_unified(mock_get_model, mock_get_layers, mock_get_pp_group):
"""Test MTP-specific model loading with unified model approach.""" """Test MTP-specific model loading with unified model approach."""
......
vllm/v1/spec_decode/dflash.py
View file @ cde8d247
...@@ -11,7 +11,7 @@ from vllm.forward_context import set_forward_context ...@@ -11,7 +11,7 @@ from vllm.forward_context import set_forward_context
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.triton_utils import triton from vllm.triton_utils import triton
from vllm.v1.attention.backend import CommonAttentionMetadata from vllm.v1.attention.backend import CommonAttentionMetadata
from vllm.v1.spec_decode.eagle import SpecDecodeBaseProposer from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
from vllm.v1.spec_decode.utils import copy_and_expand_dflash_inputs_kernel from vllm.v1.spec_decode.utils import copy_and_expand_dflash_inputs_kernel
logger = init_logger(__name__) logger = init_logger(__name__)
......
vllm/v1/spec_decode/draft_model.py
View file @ cde8d247
...@@ -9,7 +9,7 @@ from vllm.config import VllmConfig ...@@ -9,7 +9,7 @@ from vllm.config import VllmConfig
from vllm.config.utils import replace from vllm.config.utils import replace
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model from vllm.model_executor.model_loader import get_model
from vllm.v1.spec_decode.eagle import SpecDecodeBaseProposer from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
logger = init_logger(__name__) logger = init_logger(__name__)
......
vllm/v1/spec_decode/eagle.py
View file @ cde8d247
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ast
from importlib.util import find_spec
from typing import Any, cast
import numpy as np
import torch import torch
import torch.nn as nn
from vllm.config import ( from vllm.config import VllmConfig
CUDAGraphMode, from vllm.v1.spec_decode.llm_base_proposer import SpecDecodeBaseProposer
VllmConfig,
get_layers_from_vllm_config,
replace,
)
from vllm.distributed.parallel_state import get_pp_group
from vllm.forward_context import set_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models import supports_multimodal
from vllm.model_executor.models.deepseek_eagle3 import Eagle3DeepseekV2ForCausalLM
from vllm.model_executor.models.interfaces import SupportsMultiModal
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.model_executor.models.qwen3_dflash import DFlashQwen3ForCausalLM
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.platforms import current_platform
from vllm.utils.platform_utils import is_pin_memory_available
from vllm.v1.attention.backend import CommonAttentionMetadata
from vllm.v1.attention.backends.registry import AttentionBackendEnum
from vllm.v1.attention.backends.tree_attn import (
TreeAttentionMetadata,
TreeAttentionMetadataBuilder,
)
from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
from vllm.v1.kv_cache_interface import KVCacheConfig, UniformTypeKVCacheSpecs
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.sampler import _SAMPLING_EPS
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm.v1.spec_decode.utils import (
PADDING_SLOT_ID,
compute_new_slot_mapping,
copy_and_expand_eagle_inputs_kernel,
eagle_prepare_inputs_padded_kernel,
eagle_prepare_next_token_padded_kernel,
eagle_step_update_slot_mapping_and_metadata,
extend_all_queries_by_N,
next_power_of_2,
)
from vllm.v1.utils import CpuGpuBuffer
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from vllm.v1.worker.utils import AttentionGroup
logger = init_logger(__name__)
class SpecDecodeBaseProposer:
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
pass_hidden_states_to_model: bool,
runner=None,
):
self.vllm_config = vllm_config
assert vllm_config.speculative_config is not None
self.speculative_config = vllm_config.speculative_config
self.draft_model_config = self.speculative_config.draft_model_config
self.method = self.speculative_config.method
self.pass_hidden_states_to_model = pass_hidden_states_to_model
self.device = device
self.dtype = vllm_config.model_config.dtype
self.max_model_len = vllm_config.model_config.max_model_len
self.dp_rank = vllm_config.parallel_config.data_parallel_rank
self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
# 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()
self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
# 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)
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 - (
1 if (self.pass_hidden_states_to_model and self.method != "dflash") else 0
)
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()
self.use_local_argmax_reduction: bool = (
self.speculative_config.use_local_argmax_reduction
)
self.max_batch_size = vllm_config.scheduler_config.max_num_seqs
self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
self.token_arange_np = np.arange(self.max_num_tokens)
# 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
# Multi-modal data support
self.mm_registry = MULTIMODAL_REGISTRY
self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
vllm_config.model_config
)
self.draft_attn_groups: list[AttentionGroup] = []
self.kv_cache_gid: int = -1
self.eagle3_use_aux_hidden_state: bool = (
self._get_eagle3_use_aux_hidden_state_from_config()
)
self.compilation_config = self.vllm_config.compilation_config
# 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)
# persistent buffers for cuda graph
self.input_ids = torch.zeros(
self.max_num_tokens, dtype=torch.int32, device=device
)
# 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
self.uses_xdrope_dim = self.vllm_config.model_config.uses_xdrope_dim
self.draft_uses_xdrope_dim = self.draft_model_config.uses_xdrope_dim
if self.uses_mrope:
# 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
self.mrope_positions = torch.zeros(
(3, self.max_positions + 1), dtype=torch.int64, device=device
)
elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
self.xdrope_positions = torch.zeros(
(self.uses_xdrope_dim, self.max_positions + 1),
dtype=torch.int64,
device=device,
)
else:
# RoPE need (max_num_tokens,)
self.positions = torch.zeros(
self.max_positions,
dtype=torch.int64,
device=device,
)
self.hidden_states = torch.zeros(
(self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
)
# Will be set when we initialize the attention backend
self.block_size: int = -1
# We need +1 here because the arange is used to set query_start_loc,
# which has one more element than batch_size.
max_num_slots_for_arange = max(self.max_batch_size + 1, self.max_num_tokens)
self.arange = torch.arange(
max_num_slots_for_arange, device=device, dtype=torch.int32
)
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
)
self.inputs_embeds = torch.zeros(
(self.max_num_tokens, self.inputs_embeds_size),
dtype=self.dtype,
device=device,
)
self.backup_next_token_ids = CpuGpuBuffer(
self.max_batch_size,
dtype=torch.int32,
pin_memory=is_pin_memory_available(),
device=device,
with_numpy=True,
)
self._slot_mapping_buffer = torch.zeros(
self.max_positions,
dtype=torch.int64,
device=device,
)
# Determine allowed attention backends once during initialization.
self.allowed_attn_types: tuple | None = None
if current_platform.is_rocm():
from vllm.v1.attention.backends.mla.indexer import (
DeepseekV32IndexerMetadata,
)
from vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse import (
ROCMAiterMLASparseMetadata,
)
from vllm.v1.attention.backends.rocm_attn import RocmAttentionMetadata
rocm_types = [
TritonAttentionMetadata,
RocmAttentionMetadata,
ROCMAiterMLASparseMetadata,
DeepseekV32IndexerMetadata,
]
# ROCM_AITER_FA is an optional backend
# 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(
AttentionBackendEnum.ROCM_AITER_FA.get_path(include_classname=False)
):
from vllm.v1.attention.backends.rocm_aiter_fa import (
AiterFlashAttentionMetadata,
)
rocm_types.append(AiterFlashAttentionMetadata)
# TRITON_MLA backend support for MLA models (e.g., DeepSeek)
from vllm.model_executor.layers.attention.mla_attention import (
MLACommonMetadata,
)
rocm_types.append(MLACommonMetadata)
# FlexAttention backend support
from vllm.v1.attention.backends.flex_attention import FlexAttentionMetadata
rocm_types.append(FlexAttentionMetadata)
self.allowed_attn_types = tuple(rocm_types)
# Parse the speculative token tree.
spec_token_tree = self.speculative_config.speculative_token_tree
assert spec_token_tree is not None
self.tree_choices: list[tuple[int, ...]] = ast.literal_eval(spec_token_tree)
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):
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]
)
# 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(self.max_batch_size, 1)
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
# 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"):
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 "
"`pard_token`, `ptd_token_id`, or "
"`dflash_config.mask_token_id` specified in its config.json."
)
if self.pass_hidden_states_to_model:
self.parallel_drafting_hidden_state_tensor = torch.empty(
self.hidden_size, dtype=self.dtype, device=self.device
)
def _get_positions(self, num_tokens: int):
if self.uses_mrope:
return self.mrope_positions[:, :num_tokens]
if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
return self.xdrope_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
elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
self.xdrope_positions[:, :num_tokens] = positions
else:
# 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]
self.positions[:num_tokens] = positions
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]
return {name: view for name in self._draft_attn_layer_names}
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)
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)
def propose(
self,
# [num_tokens]
target_token_ids: torch.Tensor,
# [num_tokens] or [3, num_tokens] when M-RoPE is enabled
target_positions: torch.Tensor,
# [num_tokens, hidden_size]
target_hidden_states: torch.Tensor,
# [batch_size]
next_token_ids: torch.Tensor,
token_indices_to_sample: torch.Tensor | None,
common_attn_metadata: CommonAttentionMetadata,
sampling_metadata: SamplingMetadata,
mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
num_rejected_tokens_gpu: torch.Tensor | None = None,
slot_mappings: dict[str, torch.Tensor]
| list[dict[str, torch.Tensor]]
| None = None,
) -> torch.Tensor:
batch_size = common_attn_metadata.batch_size()
if self.method in ("eagle3", "dflash"):
assert isinstance(
self.model,
(
Eagle3LlamaForCausalLM,
Eagle3DeepseekV2ForCausalLM,
DFlashQwen3ForCausalLM,
),
)
target_hidden_states = self.model.combine_hidden_states(
target_hidden_states
)
assert target_hidden_states.shape[-1] == self.hidden_size
num_tokens, token_indices_to_sample, common_attn_metadata = (
self.set_inputs_first_pass(
target_token_ids=target_token_ids,
next_token_ids=next_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
token_indices_to_sample=token_indices_to_sample,
cad=common_attn_metadata,
num_rejected_tokens_gpu=num_rejected_tokens_gpu,
)
)
per_group_attn_metadata, per_layer_attn_metadata = (
self.build_per_group_and_layer_attn_metadata(common_attn_metadata)
)
cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
self._determine_batch_execution_and_padding(num_tokens)
)
model_kwargs, slot_mapping_size = self.build_model_inputs_first_pass(
num_tokens, num_input_tokens, mm_embed_inputs
)
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
num_tokens_across_dp=num_tokens_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(
slot_mapping_size, common_attn_metadata.slot_mapping
),
):
ret_hidden_states = self.model(**model_kwargs)
if not self.model_returns_tuple():
last_hidden_states = ret_hidden_states
hidden_states = last_hidden_states
else:
last_hidden_states, hidden_states = ret_hidden_states
sample_hidden_states = last_hidden_states[token_indices_to_sample]
# Early exit if there is only one draft token to be generated.
if self.num_speculative_tokens == 1 or self.parallel_drafting:
draft_token_ids = self._greedy_sample(sample_hidden_states)
return draft_token_ids.view(-1, self.num_speculative_tokens)
if self.uses_mrope:
positions = self.mrope_positions[:, token_indices_to_sample]
else:
positions = self.positions[token_indices_to_sample]
hidden_states = hidden_states[token_indices_to_sample]
if any(isinstance(md, TreeAttentionMetadata) for md in per_group_attn_metadata):
# Draft using tree attention - requires full logits for top-k
logits = self.model.compute_logits(sample_hidden_states)
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,
slot_mappings=slot_mappings,
)
# [batch_size, num_tree_tokens]
return torch.cat(draft_token_ids_list, dim=1)
draft_token_ids = self._greedy_sample(sample_hidden_states)
if self.allowed_attn_types is not None:
for group_md in per_group_attn_metadata:
if not isinstance(group_md, self.allowed_attn_types):
raise ValueError(
f"Unsupported attention metadata type for speculative "
"decoding with num_speculative_tokens > 1: "
f"{type(group_md)}. Supported types are: "
f"{self.allowed_attn_types}"
)
# Generate the remaining draft tokens.
draft_token_ids_list = [draft_token_ids]
cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
self._determine_batch_execution_and_padding(batch_size)
)
common_attn_metadata.num_actual_tokens = batch_size
common_attn_metadata.max_query_len = 1
common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
self.token_arange_np[: batch_size + 1]
).clone()
# 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
block_size = self.block_size
assert block_size > 0, "block_size has not been initialized."
for token_index in range(self.num_speculative_tokens - 1):
# Update the inputs.
# cast to int32 is crucial when eagle model is compiled.
# tensor.argmax() returns int64 by default.
input_ids = draft_token_ids_list[-1].int()
# 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
if self.uses_mrope:
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]
else:
positions = self.positions[:batch_size]
# 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
)
# 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
# Rebuild attention metadata
_, per_layer_attn_metadata = self.build_per_group_and_layer_attn_metadata(
common_attn_metadata, draft_index=token_index + 1
)
# copy inputs to buffer for cudagraph
self.input_ids[:batch_size] = input_ids
self.hidden_states[:batch_size] = hidden_states
if self.supports_mm_inputs:
self.inputs_embeds[:batch_size] = self.model.embed_input_ids(input_ids)
input_ids = None
inputs_embeds = self.inputs_embeds[:input_batch_size]
else:
input_ids = self.input_ids[:input_batch_size]
inputs_embeds = None
# Run the model.
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]
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=input_batch_size,
num_tokens_across_dp=batch_size_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(input_batch_size),
):
ret_hidden_states = self.model(**model_kwargs)
if not self.model_returns_tuple():
last_hidden_states = ret_hidden_states
hidden_states = ret_hidden_states
else:
last_hidden_states, hidden_states = ret_hidden_states
hidden_states = hidden_states[:batch_size]
draft_token_ids = self._greedy_sample(last_hidden_states[:batch_size])
draft_token_ids_list.append(draft_token_ids)
# [batch_size, num_speculative_tokens]
draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
return draft_token_ids
def set_inputs_first_pass(
self,
target_token_ids: torch.Tensor,
next_token_ids: torch.Tensor,
target_positions: torch.Tensor,
target_hidden_states: torch.Tensor,
token_indices_to_sample: torch.Tensor | None,
cad: CommonAttentionMetadata,
num_rejected_tokens_gpu: torch.Tensor | None,
) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
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, 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
)
token_indices_to_sample = torch.empty(
batch_size * self.extra_slots_per_request,
dtype=torch.int32,
device=self.device,
)
# 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.
assert self.block_size > 0, "block_size has not been initialized."
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
],
block_size=self.block_size,
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,
)
return total_num_output_tokens, token_indices_to_sample, new_cad
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_group_and_layer_attn_metadata(
self, common_attn_metadata: CommonAttentionMetadata, draft_index: int = 0
) -> tuple[list[object], dict[str, object]]:
per_group_attn_metadata: list[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
)
per_group_attn_metadata.append(attn_metadata)
for layer_name in attn_group.layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
return per_group_attn_metadata, per_layer_attn_metadata
def model_returns_tuple(self) -> bool:
return self.method not in ("mtp", "draft_model", "dflash")
def prepare_next_token_ids_cpu(
self,
sampled_token_ids: list[list[int]],
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
num_scheduled_tokens: dict[str, int],
) -> torch.Tensor:
"""
This function is used to prepare the inputs for speculative decoding.
It calculates the next token ids for each request based on the sampled
token ids from the CPU. If a request has no sampled token ids (e.g.,
during the initial decoding steps), it falls back to using the request
state to get the next token id.
"""
req_ids = gpu_input_batch.req_ids
next_token_ids: list[int] = []
for i, token_ids in enumerate(sampled_token_ids):
if token_ids:
# Common case.
next_token_id = token_ids[-1]
else:
# Partial prefill (rare case).
# Get the next token id from the request state.
req_id = req_ids[i]
req_state = requests[req_id]
seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
next_token_id = req_state.get_token_id(seq_len)
next_token_ids.append(next_token_id)
next_token_ids = torch.tensor(
next_token_ids, dtype=torch.int32, device=self.input_ids.device
)
return next_token_ids
def prepare_next_token_ids_padded(
self,
sampled_token_ids: torch.Tensor,
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
discard_request_mask: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
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. This is denoted
the "backup" token id. It also counts rejected tokens via `sampled_token_ids`.
"""
# Precompute get_token_id for when there is no valid next token
num_reqs = gpu_input_batch.num_reqs
seq_lens_list = (gpu_input_batch.num_tokens_no_spec[:num_reqs] - 1).tolist()
self.backup_next_token_ids.np[:num_reqs] = np.array(
[
requests[gpu_input_batch.req_ids[i]].get_token_id(seq_lens_list[i])
for i in range(num_reqs)
],
dtype=np.int32,
)
self.backup_next_token_ids.copy_to_gpu(num_reqs)
backup_tokens_gpu = self.backup_next_token_ids.gpu
batch_size, num_tokens = sampled_token_ids.shape
device = sampled_token_ids.device
assert discard_request_mask.dtype == torch.bool
assert backup_tokens_gpu.dtype == torch.int32
next_token_ids = torch.empty(batch_size, dtype=torch.int32, device=device)
valid_sampled_tokens_count = next_token_ids.new_empty(batch_size)
# Kernel grid: one program per request (row)
grid = (batch_size,)
# Find the next power of 2 for block sizes
BLOCK_SIZE_TOKENS = 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,
)
return next_token_ids, valid_sampled_tokens_count
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]:
"""
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.
"""
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
)
num_rejected_tokens_gpu = torch.empty(
(num_reqs,), dtype=torch.int32, device=device
)
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,
num_rejected_tokens_gpu,
num_reqs,
)
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
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,
_seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
_num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
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.max_seq_len,
block_table_tensor=common_attn_metadata.block_table_tensor,
slot_mapping=common_attn_metadata.slot_mapping[:total_num_tokens],
causal=True,
dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
)
return (
spec_common_attn_metadata,
token_indices_to_sample,
num_rejected_tokens_gpu,
)
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,
slot_mappings: dict[str, torch.Tensor]
| list[dict[str, torch.Tensor]]
| None = None,
) -> list[torch.Tensor]:
tree_attn_metadata_builder = self.draft_attn_groups[0].get_metadata_builder()
assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
total_num_drafts = self.cu_drafts_per_level[0]
level_num_drafts = total_num_drafts
# Sample a draft token for each child at the tree root level.
num_children = self.child_drafts_per_level[0]
if num_children == 1:
draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
else:
draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
batch_size, -1
)
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.
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
)
# Precompute the draft token positions.
flattened_draft_positions = (
positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
)
tree_depth = len(self.cu_drafts_per_level)
for level in range(tree_depth - 1):
# Get draft positions for RoPE.
draft_positions = positions + (level + 1)
exceeds_max_model_len = (positions + total_num_drafts) >= 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.
draft_positions = torch.where(
exceeds_max_model_len,
0,
draft_positions,
).view(batch_size, -1)
if level_num_drafts > 1:
# Repeat the positions for each draft at this level.
draft_positions = draft_positions.repeat_interleave(
level_num_drafts, dim=1
)
if num_children > 1:
# Repeat draft hidden states for each child.
draft_hidden_states = draft_hidden_states.repeat_interleave(
num_children, dim=1
)
# Concatenate the draft tokens, positions, and hidden states.
tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
tree_hidden_states = torch.cat(
[tree_hidden_states, draft_hidden_states], dim=1
)
# Build new attention metadata for the next level of drafts.
# This is necessary to support tree attention.
query_len = total_num_drafts
common_attn_metadata = replace(
common_attn_metadata,
query_start_loc=query_len * self.arange[: batch_size + 1],
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, draft_index=level + 1
)
# Apply new attention metadata to all draft layers.
per_layer_attn_metadata = {}
for attn_group in self.draft_attn_groups:
for layer_name in attn_group.layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
# Consider max model length.
attn_metadata.max_seq_len = min(
attn_metadata.max_seq_len, self.max_model_len
)
# 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.
block_size = tree_attn_metadata_builder.kv_cache_spec.block_size
query_positions = flattened_draft_positions[:, level : level + query_len]
block_numbers = query_positions // block_size
block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
slot_mapping = block_ids * block_size + query_positions % block_size
# 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)
self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
num_tokens
)
num_input_tokens = batch_desc.num_tokens
# Run the model.
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(
num_input_tokens, attn_metadata.slot_mapping
),
):
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(
batch_size, query_len, -1
)[:, -level_num_drafts:]
draft_last_hidden_states = last_hidden_states[:num_tokens].view(
batch_size, query_len, -1
)[:, -level_num_drafts:]
# Get the output logits for the draft tokens.
logits = self.model.compute_logits(
draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
)
# 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:
draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
batch_size, -1
)
draft_token_ids_list.append(draft_token_ids)
# Update the # drafts counters for the next tree level.
level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
total_num_drafts = self.cu_drafts_per_level[level + 1]
return draft_token_ids_list
def prepare_inputs(
self,
common_attn_metadata: CommonAttentionMetadata,
sampled_token_ids: list[list[int]],
num_draft_tokens: list[int],
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
"""
This function is used to prepare the inputs for speculative decoding.
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}:
# [0, q1, q1 + q2, q1 + q2 + q3]
# 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}:
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
# common_attn_metadata.seq_lens{_cpu}:
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
# token_indices: [0, 1, ..., q1 - n1 - 1,
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
num_rejected_tokens = [
n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
for i, n in enumerate(num_draft_tokens)
]
num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
device = common_attn_metadata.query_start_loc.device
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
# [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,
dtype=torch.int32,
pin_memory=is_pin_memory_available(),
)
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__
new_query_start_locs_expanded = np.repeat(
new_query_start_loc_np[:-1], new_num_tokens_per_req_np
)
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
# _r1_ ____r2____ ___r3__
token_offsets = (
self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
)
# 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(
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
)
# Final token indices are:
# [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
token_indices_np = token_offsets + old_query_start_locs_expanded
token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
spec_common_attn_metadata = CommonAttentionMetadata(
query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
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,
_num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
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=new_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,
dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
)
return spec_common_attn_metadata, token_indices
def get_model_name(self, model: nn.Module) -> str:
if hasattr(model, "module"): # multi-GPU
model = model.module
return model.__class__.__name__
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
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
draft_vllm_config = self._create_draft_vllm_config()
with set_model_tag("eagle_head"):
model = get_model(
vllm_config=draft_vllm_config,
model_config=self.speculative_config.draft_model_config,
load_config=self.speculative_config.draft_load_config,
)
return model
def load_model(self, target_model: nn.Module) -> None:
target_attn_layer_names = set(
get_layers_from_vllm_config(
self.vllm_config,
AttentionLayerBase, # type: ignore[type-abstract]
).keys()
)
self.model = self._get_model()
# 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]
)
self._draft_attn_layer_names = (
set(all_attn_layers.keys()) - target_attn_layer_names
)
if self.supports_mm_inputs:
# Even if the target model is multimodal, we can also use
# text-only draft models
try:
dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
except (NotImplementedError, AttributeError, TypeError):
logger.warning(
"Draft model does not support multimodal inputs, "
"falling back to text-only mode"
)
self.supports_mm_inputs = False
if supports_multimodal(target_model):
# handle multimodality
assert hasattr(target_model, "config")
if self.get_model_name(target_model) in [
"Exaone4_5_ForConditionalGeneration",
"GlmOcrForConditionalGeneration",
"HunYuanVLForConditionalGeneration",
"Qwen2_5_VLForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3VLForConditionalGeneration",
"Qwen3VLMoeForConditionalGeneration",
"Gemma4ForConditionalGeneration",
]:
self.model.config.image_token_index = target_model.config.image_token_id
elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":
self.model.config.image_token_index = (
target_model.config.vision_config.image_token_id
)
elif self.get_model_name(target_model) == "KimiK25ForConditionalGeneration":
self.model.config.image_token_index = (
target_model.config.media_placeholder_token_id
)
else:
self.model.config.image_token_index = (
target_model.config.image_token_index
)
target_language_model = cast(
SupportsMultiModal, target_model
).get_language_model()
else:
target_language_model = target_model
self._maybe_share_embeddings(target_language_model)
self._maybe_share_lm_head(target_language_model)
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)
)
else:
self.parallel_drafting_hidden_state_tensor.copy_(flat_mask)
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.
"""
if get_pp_group().world_size == 1:
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
else:
raise AttributeError(
"Target model does not have 'embed_tokens' or 'embedding' attribute"
)
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)
# TODO: Offload to CPU for comparison to avoid extra GPU memory
# usage in CI testing environments with limited GPU memory
and torch.equal(
target_embed_tokens.weight.cpu(),
self.model.model.embed_tokens.weight.cpu(),
)
):
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."
)
else:
# MTP model
share_embeddings = True
logger.info(
"Detected MTP model. "
"Sharing target model embedding weights with the draft model."
)
if share_embeddings:
if hasattr(self.model.model, "embed_tokens"):
del self.model.model.embed_tokens
self.model.model.embed_tokens = target_embed_tokens
else:
logger.info(
"The draft model's vocab embedding will be loaded separately"
" from the target model."
)
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.
"""
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 hasattr(target_language_model.lm_head, "weight")
and hasattr(self.model.lm_head, "weight")
and isinstance(target_language_model.lm_head.weight, torch.Tensor)
and isinstance(self.model.lm_head.weight, torch.Tensor)
# TODO: Offload to CPU for comparison to avoid extra GPU memory
# usage in CI testing environments with limited GPU memory
and torch.equal(
target_language_model.lm_head.weight.cpu(),
self.model.lm_head.weight.cpu(),
)
):
share_lm_head = True
logger.info(
"Detected EAGLE model with lm_head identical to the target model. "
"Sharing target model lm_head weights with the draft model."
)
else:
logger.info(
"Detected EAGLE model with distinct lm_head weights. "
"Keeping separate lm_head weights from the target model."
)
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
# 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."
)
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))."
)
@torch.inference_mode()
def dummy_run(
self,
num_tokens: int,
use_cudagraphs: bool = True,
is_graph_capturing: bool = False,
slot_mappings: dict[str, torch.Tensor] | None = None,
) -> None:
# FIXME: when using tree-based specdec, adjust number of forward-passes
# according to the depth of the tree.
only_one_forward_pass = is_graph_capturing or self.parallel_drafting
for fwd_idx in range(
1 if only_one_forward_pass else self.num_speculative_tokens
):
if fwd_idx <= 1:
cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
self._determine_batch_execution_and_padding(
num_tokens, use_cudagraphs=use_cudagraphs
)
)
# Make sure to use EAGLE's own buffer during cudagraph capture.
if (
self._draft_attn_layer_names
and slot_mappings is not None
and next(iter(self._draft_attn_layer_names)) in slot_mappings
):
slot_mapping_dict = self._get_slot_mapping(num_input_tokens)
else:
slot_mapping_dict = slot_mappings or {}
with set_forward_context(
None,
self.vllm_config,
num_tokens=num_input_tokens,
num_tokens_across_dp=num_tokens_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=slot_mapping_dict,
):
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
kwargs = dict(
input_ids=input_ids,
positions=self._get_positions(num_input_tokens),
inputs_embeds=inputs_embeds,
)
if self.pass_hidden_states_to_model:
kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
self.model(**kwargs)
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
def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
"""
Validate that all drafting layers belong to the same KVCacheGroup.
Need this assumption to ensure all drafting 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
assert (
len(
set(
[
kv_cache_groups[layer_name]
for layer_name in self._draft_attn_layer_names
]
)
)
== 1
), "All drafting layers should belong to the same kv cache group"
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())
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)
def _determine_batch_execution_and_padding(
self,
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),
)
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
return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
class EagleProposer(SpecDecodeBaseProposer): class EagleProposer(SpecDecodeBaseProposer):
...@@ -1745,49 +20,3 @@ class EagleProposer(SpecDecodeBaseProposer): ...@@ -1745,49 +20,3 @@ class EagleProposer(SpecDecodeBaseProposer):
pass_hidden_states_to_model=True, pass_hidden_states_to_model=True,
runner=runner, runner=runner,
) )
# 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.
# 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
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)
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_()
# 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)
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
vllm/v1/spec_decode/llm_base_proposer.py 0 → 100644
View file @ cde8d247
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ast
from importlib.util import find_spec
from typing import Any, cast
import numpy as np
import torch
import torch.nn as nn
from vllm.config import (
CUDAGraphMode,
VllmConfig,
get_layers_from_vllm_config,
replace,
)
from vllm.distributed.parallel_state import get_pp_group
from vllm.forward_context import set_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models import supports_multimodal
from vllm.model_executor.models.deepseek_eagle3 import Eagle3DeepseekV2ForCausalLM
from vllm.model_executor.models.interfaces import SupportsMultiModal
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.model_executor.models.qwen3_dflash import DFlashQwen3ForCausalLM
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.platforms import current_platform
from vllm.utils.platform_utils import is_pin_memory_available
from vllm.v1.attention.backend import CommonAttentionMetadata
from vllm.v1.attention.backends.registry import AttentionBackendEnum
from vllm.v1.attention.backends.tree_attn import (
TreeAttentionMetadata,
TreeAttentionMetadataBuilder,
)
from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
from vllm.v1.kv_cache_interface import KVCacheConfig, UniformTypeKVCacheSpecs
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.sampler import _SAMPLING_EPS
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm.v1.spec_decode.utils import (
PADDING_SLOT_ID,
compute_new_slot_mapping,
copy_and_expand_eagle_inputs_kernel,
eagle_prepare_inputs_padded_kernel,
eagle_prepare_next_token_padded_kernel,
eagle_step_update_slot_mapping_and_metadata,
extend_all_queries_by_N,
next_power_of_2,
)
from vllm.v1.utils import CpuGpuBuffer
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from vllm.v1.worker.utils import AttentionGroup
logger = init_logger(__name__)
class SpecDecodeBaseProposer:
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
pass_hidden_states_to_model: bool,
runner=None,
):
self.vllm_config = vllm_config
assert vllm_config.speculative_config is not None
self.speculative_config = vllm_config.speculative_config
self.draft_model_config = self.speculative_config.draft_model_config
self.method = self.speculative_config.method
self.pass_hidden_states_to_model = pass_hidden_states_to_model
self.device = device
self.dtype = vllm_config.model_config.dtype
self.max_model_len = vllm_config.model_config.max_model_len
self.dp_rank = vllm_config.parallel_config.data_parallel_rank
self.num_speculative_tokens = self.speculative_config.num_speculative_tokens
# 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()
self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
# 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)
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 - (
1 if (self.pass_hidden_states_to_model and self.method != "dflash") else 0
)
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()
self.use_local_argmax_reduction: bool = (
self.speculative_config.use_local_argmax_reduction
)
self.max_batch_size = vllm_config.scheduler_config.max_num_seqs
self.max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
self.token_arange_np = np.arange(self.max_num_tokens)
# 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
# Multi-modal data support
self.mm_registry = MULTIMODAL_REGISTRY
self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
vllm_config.model_config
)
self.draft_attn_groups: list[AttentionGroup] = []
self.kv_cache_gid: int = -1
self.eagle3_use_aux_hidden_state: bool = (
self._get_eagle3_use_aux_hidden_state_from_config()
)
self.compilation_config = self.vllm_config.compilation_config
# 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)
# persistent buffers for cuda graph
self.input_ids = torch.zeros(
self.max_num_tokens, dtype=torch.int32, device=device
)
# 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
self.uses_xdrope_dim = self.vllm_config.model_config.uses_xdrope_dim
self.draft_uses_xdrope_dim = self.draft_model_config.uses_xdrope_dim
if self.uses_mrope:
# 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
self.mrope_positions = torch.zeros(
(3, self.max_positions + 1), dtype=torch.int64, device=device
)
elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
self.xdrope_positions = torch.zeros(
(self.uses_xdrope_dim, self.max_positions + 1),
dtype=torch.int64,
device=device,
)
else:
# RoPE need (max_num_tokens,)
self.positions = torch.zeros(
self.max_positions,
dtype=torch.int64,
device=device,
)
self.hidden_states = torch.zeros(
(self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=device
)
# Will be set when we initialize the attention backend
self.block_size: int = -1
# We need +1 here because the arange is used to set query_start_loc,
# which has one more element than batch_size.
max_num_slots_for_arange = max(self.max_batch_size + 1, self.max_num_tokens)
self.arange = torch.arange(
max_num_slots_for_arange, device=device, dtype=torch.int32
)
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
)
self.inputs_embeds = torch.zeros(
(self.max_num_tokens, self.inputs_embeds_size),
dtype=self.dtype,
device=device,
)
self.backup_next_token_ids = CpuGpuBuffer(
self.max_batch_size,
dtype=torch.int32,
pin_memory=is_pin_memory_available(),
device=device,
with_numpy=True,
)
self._slot_mapping_buffer = torch.zeros(
self.max_positions,
dtype=torch.int64,
device=device,
)
# Determine allowed attention backends once during initialization.
self.allowed_attn_types: tuple | None = None
if current_platform.is_rocm():
from vllm.v1.attention.backends.mla.indexer import (
DeepseekV32IndexerMetadata,
)
from vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse import (
ROCMAiterMLASparseMetadata,
)
from vllm.v1.attention.backends.rocm_attn import RocmAttentionMetadata
rocm_types = [
TritonAttentionMetadata,
RocmAttentionMetadata,
ROCMAiterMLASparseMetadata,
DeepseekV32IndexerMetadata,
]
# ROCM_AITER_FA is an optional backend
# 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(
AttentionBackendEnum.ROCM_AITER_FA.get_path(include_classname=False)
):
from vllm.v1.attention.backends.rocm_aiter_fa import (
AiterFlashAttentionMetadata,
)
rocm_types.append(AiterFlashAttentionMetadata)
# TRITON_MLA backend support for MLA models (e.g., DeepSeek)
from vllm.model_executor.layers.attention.mla_attention import (
MLACommonMetadata,
)
rocm_types.append(MLACommonMetadata)
# FlexAttention backend support
from vllm.v1.attention.backends.flex_attention import FlexAttentionMetadata
rocm_types.append(FlexAttentionMetadata)
self.allowed_attn_types = tuple(rocm_types)
# Parse the speculative token tree.
spec_token_tree = self.speculative_config.speculative_token_tree
assert spec_token_tree is not None
self.tree_choices: list[tuple[int, ...]] = ast.literal_eval(spec_token_tree)
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):
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]
)
# 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(self.max_batch_size, 1)
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
# 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"):
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 "
"`pard_token`, `ptd_token_id`, or "
"`dflash_config.mask_token_id` specified in its config.json."
)
if self.pass_hidden_states_to_model:
self.parallel_drafting_hidden_state_tensor = torch.empty(
self.hidden_size, dtype=self.dtype, device=self.device
)
def _get_positions(self, num_tokens: int):
if self.uses_mrope:
return self.mrope_positions[:, :num_tokens]
if self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
return self.xdrope_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
elif self.uses_xdrope_dim > 0 and self.draft_uses_xdrope_dim > 0:
self.xdrope_positions[:, :num_tokens] = positions
else:
# 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]
self.positions[:num_tokens] = positions
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]
return {name: view for name in self._draft_attn_layer_names}
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)
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)
def propose(
self,
# [num_tokens]
target_token_ids: torch.Tensor,
# [num_tokens] or [3, num_tokens] when M-RoPE is enabled
target_positions: torch.Tensor,
# [num_tokens, hidden_size]
target_hidden_states: torch.Tensor,
# [batch_size]
next_token_ids: torch.Tensor,
token_indices_to_sample: torch.Tensor | None,
common_attn_metadata: CommonAttentionMetadata,
sampling_metadata: SamplingMetadata,
mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
num_rejected_tokens_gpu: torch.Tensor | None = None,
slot_mappings: dict[str, torch.Tensor]
| list[dict[str, torch.Tensor]]
| None = None,
) -> torch.Tensor:
batch_size = common_attn_metadata.batch_size()
if self.method in ("eagle3", "dflash"):
assert isinstance(
self.model,
(
Eagle3LlamaForCausalLM,
Eagle3DeepseekV2ForCausalLM,
DFlashQwen3ForCausalLM,
),
)
target_hidden_states = self.model.combine_hidden_states(
target_hidden_states
)
assert target_hidden_states.shape[-1] == self.hidden_size
num_tokens, token_indices_to_sample, common_attn_metadata = (
self.set_inputs_first_pass(
target_token_ids=target_token_ids,
next_token_ids=next_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
token_indices_to_sample=token_indices_to_sample,
cad=common_attn_metadata,
num_rejected_tokens_gpu=num_rejected_tokens_gpu,
)
)
per_group_attn_metadata, per_layer_attn_metadata = (
self.build_per_group_and_layer_attn_metadata(common_attn_metadata)
)
cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
self._determine_batch_execution_and_padding(num_tokens)
)
model_kwargs, slot_mapping_size = self.build_model_inputs_first_pass(
num_tokens, num_input_tokens, mm_embed_inputs
)
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
num_tokens_across_dp=num_tokens_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(
slot_mapping_size, common_attn_metadata.slot_mapping
),
):
ret_hidden_states = self.model(**model_kwargs)
if not self.model_returns_tuple():
last_hidden_states = ret_hidden_states
hidden_states = last_hidden_states
else:
last_hidden_states, hidden_states = ret_hidden_states
sample_hidden_states = last_hidden_states[token_indices_to_sample]
# Early exit if there is only one draft token to be generated.
if self.num_speculative_tokens == 1 or self.parallel_drafting:
draft_token_ids = self._greedy_sample(sample_hidden_states)
return draft_token_ids.view(-1, self.num_speculative_tokens)
if self.uses_mrope:
positions = self.mrope_positions[:, token_indices_to_sample]
else:
positions = self.positions[token_indices_to_sample]
hidden_states = hidden_states[token_indices_to_sample]
if any(isinstance(md, TreeAttentionMetadata) for md in per_group_attn_metadata):
# Draft using tree attention - requires full logits for top-k
logits = self.model.compute_logits(sample_hidden_states)
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,
slot_mappings=slot_mappings,
)
# [batch_size, num_tree_tokens]
return torch.cat(draft_token_ids_list, dim=1)
draft_token_ids = self._greedy_sample(sample_hidden_states)
if self.allowed_attn_types is not None:
for group_md in per_group_attn_metadata:
if not isinstance(group_md, self.allowed_attn_types):
raise ValueError(
f"Unsupported attention metadata type for speculative "
"decoding with num_speculative_tokens > 1: "
f"{type(group_md)}. Supported types are: "
f"{self.allowed_attn_types}"
)
# Generate the remaining draft tokens.
draft_token_ids_list = [draft_token_ids]
cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
self._determine_batch_execution_and_padding(batch_size)
)
common_attn_metadata.num_actual_tokens = batch_size
common_attn_metadata.max_query_len = 1
common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
common_attn_metadata.query_start_loc_cpu = torch.from_numpy(
self.token_arange_np[: batch_size + 1]
).clone()
# 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
block_size = self.block_size
assert block_size > 0, "block_size has not been initialized."
for token_index in range(self.num_speculative_tokens - 1):
# Update the inputs.
# cast to int32 is crucial when eagle model is compiled.
# tensor.argmax() returns int64 by default.
input_ids = draft_token_ids_list[-1].int()
# 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
if self.uses_mrope:
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]
else:
positions = self.positions[:batch_size]
# 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
)
# 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
# Rebuild attention metadata
_, per_layer_attn_metadata = self.build_per_group_and_layer_attn_metadata(
common_attn_metadata, draft_index=token_index + 1
)
# copy inputs to buffer for cudagraph
self.input_ids[:batch_size] = input_ids
self.hidden_states[:batch_size] = hidden_states
if self.supports_mm_inputs:
self.inputs_embeds[:batch_size] = self.model.embed_input_ids(input_ids)
input_ids = None
inputs_embeds = self.inputs_embeds[:input_batch_size]
else:
input_ids = self.input_ids[:input_batch_size]
inputs_embeds = None
# Run the model.
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]
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=input_batch_size,
num_tokens_across_dp=batch_size_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(input_batch_size),
):
ret_hidden_states = self.model(**model_kwargs)
if not self.model_returns_tuple():
last_hidden_states = ret_hidden_states
hidden_states = ret_hidden_states
else:
last_hidden_states, hidden_states = ret_hidden_states
hidden_states = hidden_states[:batch_size]
draft_token_ids = self._greedy_sample(last_hidden_states[:batch_size])
draft_token_ids_list.append(draft_token_ids)
# [batch_size, num_speculative_tokens]
draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
return draft_token_ids
def set_inputs_first_pass(
self,
target_token_ids: torch.Tensor,
next_token_ids: torch.Tensor,
target_positions: torch.Tensor,
target_hidden_states: torch.Tensor,
token_indices_to_sample: torch.Tensor | None,
cad: CommonAttentionMetadata,
num_rejected_tokens_gpu: torch.Tensor | None,
) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
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, 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
)
token_indices_to_sample = torch.empty(
batch_size * self.extra_slots_per_request,
dtype=torch.int32,
device=self.device,
)
# 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.
assert self.block_size > 0, "block_size has not been initialized."
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
],
block_size=self.block_size,
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,
)
return total_num_output_tokens, token_indices_to_sample, new_cad
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_group_and_layer_attn_metadata(
self, common_attn_metadata: CommonAttentionMetadata, draft_index: int = 0
) -> tuple[list[object], dict[str, object]]:
per_group_attn_metadata: list[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
)
per_group_attn_metadata.append(attn_metadata)
for layer_name in attn_group.layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
return per_group_attn_metadata, per_layer_attn_metadata
def model_returns_tuple(self) -> bool:
return self.method not in ("mtp", "draft_model", "dflash")
def prepare_next_token_ids_cpu(
self,
sampled_token_ids: list[list[int]],
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
num_scheduled_tokens: dict[str, int],
) -> torch.Tensor:
"""
This function is used to prepare the inputs for speculative decoding.
It calculates the next token ids for each request based on the sampled
token ids from the CPU. If a request has no sampled token ids (e.g.,
during the initial decoding steps), it falls back to using the request
state to get the next token id.
"""
req_ids = gpu_input_batch.req_ids
next_token_ids: list[int] = []
for i, token_ids in enumerate(sampled_token_ids):
if token_ids:
# Common case.
next_token_id = token_ids[-1]
else:
# Partial prefill (rare case).
# Get the next token id from the request state.
req_id = req_ids[i]
req_state = requests[req_id]
seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
next_token_id = req_state.get_token_id(seq_len)
next_token_ids.append(next_token_id)
next_token_ids = torch.tensor(
next_token_ids, dtype=torch.int32, device=self.input_ids.device
)
return next_token_ids
def prepare_next_token_ids_padded(
self,
sampled_token_ids: torch.Tensor,
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
discard_request_mask: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
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. This is denoted
the "backup" token id. It also counts rejected tokens via `sampled_token_ids`.
"""
# Precompute get_token_id for when there is no valid next token
num_reqs = gpu_input_batch.num_reqs
seq_lens_list = (gpu_input_batch.num_tokens_no_spec[:num_reqs] - 1).tolist()
self.backup_next_token_ids.np[:num_reqs] = np.array(
[
requests[gpu_input_batch.req_ids[i]].get_token_id(seq_lens_list[i])
for i in range(num_reqs)
],
dtype=np.int32,
)
self.backup_next_token_ids.copy_to_gpu(num_reqs)
backup_tokens_gpu = self.backup_next_token_ids.gpu
batch_size, num_tokens = sampled_token_ids.shape
device = sampled_token_ids.device
assert discard_request_mask.dtype == torch.bool
assert backup_tokens_gpu.dtype == torch.int32
next_token_ids = torch.empty(batch_size, dtype=torch.int32, device=device)
valid_sampled_tokens_count = next_token_ids.new_empty(batch_size)
# Kernel grid: one program per request (row)
grid = (batch_size,)
# Find the next power of 2 for block sizes
BLOCK_SIZE_TOKENS = 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,
)
return next_token_ids, valid_sampled_tokens_count
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]:
"""
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.
"""
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
)
num_rejected_tokens_gpu = torch.empty(
(num_reqs,), dtype=torch.int32, device=device
)
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,
num_rejected_tokens_gpu,
num_reqs,
)
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
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,
_seq_lens_cpu=common_attn_metadata._seq_lens_cpu,
_num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
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.max_seq_len,
block_table_tensor=common_attn_metadata.block_table_tensor,
slot_mapping=common_attn_metadata.slot_mapping[:total_num_tokens],
causal=True,
dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
)
return (
spec_common_attn_metadata,
token_indices_to_sample,
num_rejected_tokens_gpu,
)
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,
slot_mappings: dict[str, torch.Tensor]
| list[dict[str, torch.Tensor]]
| None = None,
) -> list[torch.Tensor]:
tree_attn_metadata_builder = self.draft_attn_groups[0].get_metadata_builder()
assert isinstance(tree_attn_metadata_builder, TreeAttentionMetadataBuilder)
total_num_drafts = self.cu_drafts_per_level[0]
level_num_drafts = total_num_drafts
# Sample a draft token for each child at the tree root level.
num_children = self.child_drafts_per_level[0]
if num_children == 1:
draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
else:
draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
batch_size, -1
)
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.
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
)
# Precompute the draft token positions.
flattened_draft_positions = (
positions.view(batch_size, -1) + self.tree_draft_pos_offsets[:batch_size, :]
)
tree_depth = len(self.cu_drafts_per_level)
for level in range(tree_depth - 1):
# Get draft positions for RoPE.
draft_positions = positions + (level + 1)
exceeds_max_model_len = (positions + total_num_drafts) >= 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.
draft_positions = torch.where(
exceeds_max_model_len,
0,
draft_positions,
).view(batch_size, -1)
if level_num_drafts > 1:
# Repeat the positions for each draft at this level.
draft_positions = draft_positions.repeat_interleave(
level_num_drafts, dim=1
)
if num_children > 1:
# Repeat draft hidden states for each child.
draft_hidden_states = draft_hidden_states.repeat_interleave(
num_children, dim=1
)
# Concatenate the draft tokens, positions, and hidden states.
tree_input_ids = torch.cat([tree_input_ids, draft_token_ids], dim=1)
tree_positions = torch.cat([tree_positions, draft_positions], dim=1)
tree_hidden_states = torch.cat(
[tree_hidden_states, draft_hidden_states], dim=1
)
# Build new attention metadata for the next level of drafts.
# This is necessary to support tree attention.
query_len = total_num_drafts
common_attn_metadata = replace(
common_attn_metadata,
query_start_loc=query_len * self.arange[: batch_size + 1],
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, draft_index=level + 1
)
# Apply new attention metadata to all draft layers.
per_layer_attn_metadata = {}
for attn_group in self.draft_attn_groups:
for layer_name in attn_group.layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
# Consider max model length.
attn_metadata.max_seq_len = min(
attn_metadata.max_seq_len, self.max_model_len
)
# 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.
block_size = tree_attn_metadata_builder.kv_cache_spec.block_size
query_positions = flattened_draft_positions[:, level : level + query_len]
block_numbers = query_positions // block_size
block_ids = attn_metadata.block_table.gather(dim=1, index=block_numbers)
slot_mapping = block_ids * block_size + query_positions % block_size
# 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)
self.hidden_states[:num_tokens] = tree_hidden_states.view(num_tokens, -1)
cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
num_tokens
)
num_input_tokens = batch_desc.num_tokens
# Run the model.
with set_forward_context(
per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=self._get_slot_mapping(
num_input_tokens, attn_metadata.slot_mapping
),
):
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(
batch_size, query_len, -1
)[:, -level_num_drafts:]
draft_last_hidden_states = last_hidden_states[:num_tokens].view(
batch_size, query_len, -1
)[:, -level_num_drafts:]
# Get the output logits for the draft tokens.
logits = self.model.compute_logits(
draft_last_hidden_states.reshape(batch_size * level_num_drafts, -1)
)
# 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:
draft_token_ids = torch.topk(logits, num_children, dim=-1).indices.view(
batch_size, -1
)
draft_token_ids_list.append(draft_token_ids)
# Update the # drafts counters for the next tree level.
level_num_drafts = self.cu_drafts_per_level[level + 1] - total_num_drafts
total_num_drafts = self.cu_drafts_per_level[level + 1]
return draft_token_ids_list
def prepare_inputs(
self,
common_attn_metadata: CommonAttentionMetadata,
sampled_token_ids: list[list[int]],
num_draft_tokens: list[int],
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
"""
This function is used to prepare the inputs for speculative decoding.
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}:
# [0, q1, q1 + q2, q1 + q2 + q3]
# 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}:
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
# common_attn_metadata.seq_lens{_cpu}:
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
# token_indices: [0, 1, ..., q1 - n1 - 1,
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
num_rejected_tokens = [
n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
for i, n in enumerate(num_draft_tokens)
]
num_rejected_tokens = torch.tensor(num_rejected_tokens, dtype=torch.int32)
device = common_attn_metadata.query_start_loc.device
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu - num_rejected_tokens
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
new_query_len_per_req = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
# [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,
dtype=torch.int32,
pin_memory=is_pin_memory_available(),
)
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__
new_query_start_locs_expanded = np.repeat(
new_query_start_loc_np[:-1], new_num_tokens_per_req_np
)
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
# _r1_ ____r2____ ___r3__
token_offsets = (
self.token_arange_np[:total_num_tokens] - new_query_start_locs_expanded
)
# 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(
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np
)
# Final token indices are:
# [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
token_indices_np = token_offsets + old_query_start_locs_expanded
token_indices = torch.from_numpy(token_indices_np).to(device, non_blocking=True)
spec_common_attn_metadata = CommonAttentionMetadata(
query_start_loc=new_query_start_loc_cpu.to(device, non_blocking=True),
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,
_num_computed_tokens_cpu=common_attn_metadata._num_computed_tokens_cpu,
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=new_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,
dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
)
return spec_common_attn_metadata, token_indices
def get_model_name(self, model: nn.Module) -> str:
if hasattr(model, "module"): # multi-GPU
model = model.module
return model.__class__.__name__
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
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
draft_vllm_config = self._create_draft_vllm_config()
with set_model_tag("eagle_head"):
model = get_model(
vllm_config=draft_vllm_config,
model_config=self.speculative_config.draft_model_config,
load_config=self.speculative_config.draft_load_config,
)
return model
def load_model(self, target_model: nn.Module) -> None:
target_attn_layer_names = set(
get_layers_from_vllm_config(
self.vllm_config,
AttentionLayerBase, # type: ignore[type-abstract]
).keys()
)
self.model = self._get_model()
# 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]
)
self._draft_attn_layer_names = (
set(all_attn_layers.keys()) - target_attn_layer_names
)
if self.supports_mm_inputs:
# Even if the target model is multimodal, we can also use
# text-only draft models
try:
dummy_input_ids = torch.tensor([[1]], device=self.input_ids.device)
self.model.embed_input_ids(dummy_input_ids, multimodal_embeddings=None)
except (NotImplementedError, AttributeError, TypeError):
logger.warning(
"Draft model does not support multimodal inputs, "
"falling back to text-only mode"
)
self.supports_mm_inputs = False
if supports_multimodal(target_model):
# handle multimodality
assert hasattr(target_model, "config")
if self.get_model_name(target_model) in [
"Exaone4_5_ForConditionalGeneration",
"GlmOcrForConditionalGeneration",
"HunYuanVLForConditionalGeneration",
"Qwen2_5_VLForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3VLForConditionalGeneration",
"Qwen3VLMoeForConditionalGeneration",
"Gemma4ForConditionalGeneration",
]:
self.model.config.image_token_index = target_model.config.image_token_id
elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":
self.model.config.image_token_index = (
target_model.config.vision_config.image_token_id
)
elif self.get_model_name(target_model) == "KimiK25ForConditionalGeneration":
self.model.config.image_token_index = (
target_model.config.media_placeholder_token_id
)
else:
self.model.config.image_token_index = (
target_model.config.image_token_index
)
target_language_model = cast(
SupportsMultiModal, target_model
).get_language_model()
else:
target_language_model = target_model
self._maybe_share_embeddings(target_language_model)
self._maybe_share_lm_head(target_language_model)
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)
)
else:
self.parallel_drafting_hidden_state_tensor.copy_(flat_mask)
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.
"""
if get_pp_group().world_size == 1:
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
else:
raise AttributeError(
"Target model does not have 'embed_tokens' or 'embedding' attribute"
)
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)
# TODO: Offload to CPU for comparison to avoid extra GPU memory
# usage in CI testing environments with limited GPU memory
and torch.equal(
target_embed_tokens.weight.cpu(),
self.model.model.embed_tokens.weight.cpu(),
)
):
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."
)
else:
# MTP model
share_embeddings = True
logger.info(
"Detected MTP model. "
"Sharing target model embedding weights with the draft model."
)
if share_embeddings:
if hasattr(self.model.model, "embed_tokens"):
del self.model.model.embed_tokens
self.model.model.embed_tokens = target_embed_tokens
else:
logger.info(
"The draft model's vocab embedding will be loaded separately"
" from the target model."
)
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.
"""
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 hasattr(target_language_model.lm_head, "weight")
and hasattr(self.model.lm_head, "weight")
and isinstance(target_language_model.lm_head.weight, torch.Tensor)
and isinstance(self.model.lm_head.weight, torch.Tensor)
# TODO: Offload to CPU for comparison to avoid extra GPU memory
# usage in CI testing environments with limited GPU memory
and torch.equal(
target_language_model.lm_head.weight.cpu(),
self.model.lm_head.weight.cpu(),
)
):
share_lm_head = True
logger.info(
"Detected EAGLE model with lm_head identical to the target model. "
"Sharing target model lm_head weights with the draft model."
)
else:
logger.info(
"Detected EAGLE model with distinct lm_head weights. "
"Keeping separate lm_head weights from the target model."
)
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
# 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."
)
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))."
)
@torch.inference_mode()
def dummy_run(
self,
num_tokens: int,
use_cudagraphs: bool = True,
is_graph_capturing: bool = False,
slot_mappings: dict[str, torch.Tensor] | None = None,
) -> None:
# FIXME: when using tree-based specdec, adjust number of forward-passes
# according to the depth of the tree.
only_one_forward_pass = is_graph_capturing or self.parallel_drafting
for fwd_idx in range(
1 if only_one_forward_pass else self.num_speculative_tokens
):
if fwd_idx <= 1:
cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
self._determine_batch_execution_and_padding(
num_tokens, use_cudagraphs=use_cudagraphs
)
)
# Make sure to use EAGLE's own buffer during cudagraph capture.
if (
self._draft_attn_layer_names
and slot_mappings is not None
and next(iter(self._draft_attn_layer_names)) in slot_mappings
):
slot_mapping_dict = self._get_slot_mapping(num_input_tokens)
else:
slot_mapping_dict = slot_mappings or {}
with set_forward_context(
None,
self.vllm_config,
num_tokens=num_input_tokens,
num_tokens_across_dp=num_tokens_across_dp,
cudagraph_runtime_mode=cudagraph_runtime_mode,
slot_mapping=slot_mapping_dict,
):
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
kwargs = dict(
input_ids=input_ids,
positions=self._get_positions(num_input_tokens),
inputs_embeds=inputs_embeds,
)
if self.pass_hidden_states_to_model:
kwargs["hidden_states"] = self.hidden_states[:num_input_tokens]
self.model(**kwargs)
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
def validate_same_kv_cache_group(self, kv_cache_config: KVCacheConfig) -> None:
"""
Validate that all drafting layers belong to the same KVCacheGroup.
Need this assumption to ensure all drafting 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
assert (
len(
set(
[
kv_cache_groups[layer_name]
for layer_name in self._draft_attn_layer_names
]
)
)
== 1
), "All drafting layers should belong to the same kv cache group"
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())
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)
def _determine_batch_execution_and_padding(
self,
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),
)
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
return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
# 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.
# 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
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)
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_()
# 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)
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
vllm/v1/worker/cpu_model_runner.py
View file @ cde8d247
...@@ -65,16 +65,16 @@ class CPUModelRunner(GPUModelRunner): ...@@ -65,16 +65,16 @@ class CPUModelRunner(GPUModelRunner):
# Speculative decoding fallbacks # Speculative decoding fallbacks
import vllm.v1.sample.rejection_sampler import vllm.v1.sample.rejection_sampler
import vllm.v1.spec_decode.eagle import vllm.v1.spec_decode.llm_base_proposer
import vllm.v1.spec_decode.utils import vllm.v1.spec_decode.utils
vllm.v1.spec_decode.eagle.eagle_prepare_inputs_padded_kernel = ( vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_inputs_padded_kernel = (
cpu_tl.eagle_prepare_inputs_padded_kernel cpu_tl.eagle_prepare_inputs_padded_kernel
) )
vllm.v1.spec_decode.eagle.eagle_prepare_next_token_padded_kernel = ( vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_next_token_padded_kernel = (
cpu_tl.eagle_prepare_next_token_padded_kernel cpu_tl.eagle_prepare_next_token_padded_kernel
) )
vllm.v1.spec_decode.eagle.copy_and_expand_eagle_inputs_kernel = ( vllm.v1.spec_decode.llm_base_proposer.copy_and_expand_eagle_inputs_kernel = (
cpu_tl.copy_and_expand_eagle_inputs_kernel cpu_tl.copy_and_expand_eagle_inputs_kernel
) )
vllm.v1.spec_decode.utils.eagle_step_slot_mapping_metadata_kernel = ( vllm.v1.spec_decode.utils.eagle_step_slot_mapping_metadata_kernel = (
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment