class EagleProposer:
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
runner=None,
):
self.vllm_config = vllm_config
self.speculative_config = vllm_config.speculative_config
self.draft_model_config = self.speculative_config.draft_model_config
self.method = self.speculative_config.method
self.runner = runner
self.device = device
self.dtype = vllm_config.model_config.dtype
self.max_model_len = vllm_config.model_config.max_model_len
self.block_size = vllm_config.cache_config.block_size
self.num_speculative_tokens = (
self.speculative_config.num_speculative_tokens)
self.max_num_tokens = (
vllm_config.scheduler_config.max_num_batched_tokens)
self.token_arange_np = np.arange(self.max_num_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()
# Multi-modal data support
self.mm_registry = MULTIMODAL_REGISTRY
self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
vllm_config.model_config)
self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None
self.use_cuda_graph = (not current_platform.is_xpu()
and self.vllm_config.compilation_config.level
== CompilationLevel.PIECEWISE and
not self.vllm_config.model_config.enforce_eager)
self.cudagraph_batch_sizes = list(
reversed(self.vllm_config.compilation_config.
cudagraph_capture_sizes)) if self.use_cuda_graph else []
# persistent buffers for cuda graph
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device=device)
self.uses_mrope = self.vllm_config.model_config.uses_mrope
if self.uses_mrope:
# M-RoPE need (3, max_num_tokens)
self.mrope_positions = torch.zeros((3, self.max_num_tokens),
dtype=torch.int64,
device=device)
else:
# RoPE need (max_num_tokens,)
self.positions = torch.zeros(self.max_num_tokens,
dtype=torch.int64,
device=device)
self.hidden_states = torch.zeros(
(self.max_num_tokens, self.hidden_size),
dtype=self.dtype,
device=device)
# We need +1 here because the arange is used to set query_start_loc,
# which has one more element than batch_size.
max_batch_size = vllm_config.scheduler_config.max_num_seqs
max_num_slots_for_arange = max(max_batch_size + 1, self.max_num_tokens)
self.arange = torch.arange(max_num_slots_for_arange,
device=device,
dtype=torch.int32)
self.inputs_embeds = torch.zeros(
(self.max_num_tokens, self.hidden_size),
dtype=self.dtype,
device=device)
self.backup_next_token_ids = CpuGpuBuffer(
max_batch_size,
dtype=torch.int32,
pin_memory=is_pin_memory_available(),
device=device,
with_numpy=True)
# Determine allowed attention backends once during initialization.
self.allowed_attn_types: Optional[tuple] = None
if current_platform.is_rocm():
rocm_types = [TritonAttentionMetadata, FlashAttentionMetadata]
# vllm.v1.attention.backends.rocm_aiter_fa is an optional backend
if find_spec("vllm.v1.attention.backends.rocm_aiter_fa"):
from vllm.v1.attention.backends.rocm_aiter_fa import (
AiterFlashAttentionMetadata)
rocm_types.append(AiterFlashAttentionMetadata)
self.allowed_attn_types = tuple(rocm_types)
# Parse the speculative token tree.
spec_token_tree = self.speculative_config.speculative_token_tree
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(max_batch_size, 1)
def _get_positions(self, num_tokens: int):
if self.uses_mrope:
return self.mrope_positions[:, :num_tokens]
return self.positions[:num_tokens]
def _set_positions(self, num_tokens: int, positions: torch.Tensor):
if self.uses_mrope:
self.mrope_positions[:, :num_tokens] = positions
else:
self.positions[:num_tokens] = positions
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,
last_token_indices: Optional[torch.Tensor],
common_attn_metadata: CommonAttentionMetadata,
sampling_metadata: SamplingMetadata,
mm_embed_inputs: Optional[tuple[list[torch.Tensor],
torch.Tensor]] = None,
) -> torch.Tensor:
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
if last_token_indices is None:
last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
if self.method == "eagle3":
assert isinstance(self.model, Eagle3LlamaForCausalLM)
target_hidden_states = self.model.combine_hidden_states(
target_hidden_states)
assert target_hidden_states.shape[-1] == self.hidden_size
# 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[last_token_indices] = next_token_ids
assert self.runner is not None
# FIXME: need to consider multiple kv_cache_groups
ubatch_id = dbo_current_ubatch_id()
attn_metadata_builder = \
self.runner.attn_groups[0][0].metadata_builders[ubatch_id]
attn_metadata = attn_metadata_builder.build_for_drafting(
common_attn_metadata=common_attn_metadata, draft_index=0)
# FIXME: support hybrid kv for draft model (remove separate indexer)
if self.draft_indexer_metadata_builder:
draft_indexer_metadata = (
self.draft_indexer_metadata_builder.build_for_drafting(
common_attn_metadata=common_attn_metadata,
draft_index=0,
))
else:
draft_indexer_metadata = None
# At this moment, we assume all eagle layers belong to the same KV
# cache group, thus using the same attention metadata.
per_layer_attn_metadata = {}
for layer_name in self.attn_layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
for layer_name in self.indexer_layer_names:
assert draft_indexer_metadata is not None
per_layer_attn_metadata[layer_name] = draft_indexer_metadata
if self.use_cuda_graph and \
num_tokens <= self.cudagraph_batch_sizes[-1]:
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
else:
num_input_tokens = num_tokens
# copy inputs to buffer for cudagraph
self._set_positions(num_tokens, target_positions)
self.hidden_states[:num_tokens] = target_hidden_states
if self.supports_mm_inputs:
mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)
self.inputs_embeds[:num_tokens] = self.model.get_input_embeddings(
self.input_ids[:num_tokens],
multimodal_embeddings=mm_embeds,
is_multimodal=is_mm_embed,
)
input_ids = None
inputs_embeds = self.inputs_embeds[:num_input_tokens]
else:
input_ids = self.input_ids[:num_input_tokens]
inputs_embeds = None
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens):
ret_hidden_states = self.model(
input_ids=input_ids,
positions=self._get_positions(num_input_tokens),
hidden_states=self.hidden_states[:num_input_tokens],
inputs_embeds=inputs_embeds,
)
if self.method == "mtp":
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[last_token_indices]
logits = self.model.compute_logits(sample_hidden_states)
# Early exit if there is only one draft token to be generated.
if self.num_speculative_tokens == 1:
draft_token_ids = logits.argmax(dim=-1)
return draft_token_ids.view(-1, 1)
if self.uses_mrope:
positions = target_positions[:, last_token_indices]
else:
positions = target_positions[last_token_indices]
if self.method in ("deepseek_mtp", "ernie_mtp", "longcat_flash_mtp"):
hidden_states = self.hidden_states[last_token_indices]
else:
hidden_states = hidden_states[last_token_indices]
if isinstance(attn_metadata, TreeAttentionMetadata):
# Draft using tree attention.
draft_token_ids_list = self.propose_tree(
batch_size=batch_size,
logits=logits,
positions=positions,
hidden_states=hidden_states,
common_attn_metadata=common_attn_metadata,
)
# [batch_size, num_tree_tokens]
return torch.cat(draft_token_ids_list, dim=1)
draft_token_ids = logits.argmax(dim=-1)
if self.allowed_attn_types is not None and \
not isinstance(attn_metadata, self.allowed_attn_types):
raise ValueError(
f"Unsupported attention metadata type for speculative "
"decoding with num_speculative_tokens > 1: "
f"{type(attn_metadata)}. Supported types are: "
f"{self.allowed_attn_types}")
# Generate the remaining draft tokens.
draft_token_ids_list = [draft_token_ids]
if self.use_cuda_graph and \
batch_size <= self.cudagraph_batch_sizes[-1]:
input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
else:
input_batch_size = 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()
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()
if self.uses_mrope:
positions += 1
# NOTE(woosuk): We should handle the case where the draft model
# generates tokens beyond the max model length.
# Since it is complex to remove such requests from the batch,
# we keep them in the batch but adjust the position ids
# and slot mappings to avoid the
# out-of-range access during the model execution.
# The draft tokens generated with this adjustment
# should be ignored.
exceeds_max_model_len = positions[0] >= self.max_model_len
# Mask out the position ids that exceed the max model length.
# Otherwise, we may get out-of-range error in RoPE.
clamped_positions = torch.where\
(exceeds_max_model_len.unsqueeze(0), \
torch.zeros_like(positions), positions)
else:
positions += 1
exceeds_max_model_len = positions >= self.max_model_len
clamped_positions = torch.where(exceeds_max_model_len, 0,
positions)
# Increment the sequence lengths.
common_attn_metadata.seq_lens += 1
common_attn_metadata.seq_lens_cpu += 1
# For the requests that exceed the max model length, we set the
# sequence length to 1 to minimize their overheads in attention.
common_attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len,
1)
common_attn_metadata.num_computed_tokens_cpu = \
common_attn_metadata.seq_lens_cpu - 1
# Compute the slot mapping.
if self.uses_mrope:
# all dimensions of positions are the same
block_numbers = clamped_positions[0] // self.block_size
else:
block_numbers = clamped_positions // self.block_size
block_ids = common_attn_metadata.block_table_tensor.gather(
dim=1, index=block_numbers.view(-1, 1))
block_ids = block_ids.view(-1)
if self.uses_mrope:
common_attn_metadata.slot_mapping = (
block_ids * self.block_size +
clamped_positions[0] % self.block_size)
else:
common_attn_metadata.slot_mapping = (
block_ids * self.block_size +
clamped_positions % self.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.
common_attn_metadata.slot_mapping.masked_fill_(
exceeds_max_model_len, PADDING_SLOT_ID)
# Rebuild attention metadata
attn_metadata = attn_metadata_builder.build_for_drafting( # type: ignore
common_attn_metadata=common_attn_metadata,
draft_index=token_index + 1)
for layer_name in self.attn_layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
# copy inputs to buffer for cudagraph
self.input_ids[:batch_size] = input_ids
self._set_positions(batch_size, clamped_positions)
self.hidden_states[:batch_size] = hidden_states
if self.supports_mm_inputs:
self.inputs_embeds[:batch_size] = \
self.model.get_input_embeddings(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.
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=input_batch_size):
ret_hidden_states = self.model(
input_ids=input_ids,
positions=self._get_positions(input_batch_size),
hidden_states=self.hidden_states[:input_batch_size],
inputs_embeds=inputs_embeds,
)
if self.method == "mtp":
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]
logits = self.model.compute_logits(last_hidden_states[:batch_size])
draft_token_ids = logits.argmax(dim=-1)
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 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,
common_attn_metadata: CommonAttentionMetadata,
sampled_token_ids: torch.Tensor,
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
discard_request_indices: torch.Tensor,
num_discarded_requests: int) -> \
tuple[torch.Tensor, torch.Tensor]:
"""
This function is used to prepare the inputs for speculative decoding.
It calculates the next token ids and the number of valid sampled tokens
for each request, considering the "discarded" requests whose next token
is not sampled and comes from `request.get_token_id()` instead.
It also accounts for the rejected tokens in `sampled_token_ids`.
This function must use device functions to operate on the inputs, and
should not introduce any blocking CPU-GPU synchronization.
"""
# TODO(Ben): Combine this into a custom fused kernel
# Precompute get_token_id for when there is no valid next token
num_reqs = gpu_input_batch.num_reqs
self.backup_next_token_ids.np[:num_reqs] = np.array([
requests[gpu_input_batch.req_ids[i]].get_token_id(
common_attn_metadata.seq_lens_cpu[i].item())
for i in range(num_reqs)
])
self.backup_next_token_ids.copy_to_gpu(num_reqs)
# Mask out the sampled tokens indices that should not be sampled.
discard_sampled_tokens_req_indices = \
discard_request_indices[:num_discarded_requests]
valid_sampled_token_ids_gpu = sampled_token_ids.clone()
valid_sampled_token_ids_gpu.index_fill_(
0, discard_sampled_tokens_req_indices, -1)
# Generate a mask for all valid tokens within those requests
max_gen_len = sampled_token_ids.shape[-1]
if max_gen_len == 1:
valid_mask = torch.ones_like(valid_sampled_token_ids_gpu,
dtype=torch.bool)
else:
valid_mask = (
(valid_sampled_token_ids_gpu != -1) &
(valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size))
# Count the number of valid tokens in each request
valid_sampled_tokens_count = valid_mask.sum(dim=1)
# Get the rightmost valid index per row
last_valid_indices = valid_sampled_tokens_count - 1
last_valid_indices_safe = torch.clamp(last_valid_indices, min=0)
# Get last valid token from each row
# (assume undefined state where there is no valid token)
selected_tokens = torch.gather(
valid_sampled_token_ids_gpu, 1,
last_valid_indices_safe.unsqueeze(1)).squeeze(1)
# Use last token if valid, pre-computed backup if not
batch_size = valid_sampled_token_ids_gpu.shape[0]
next_token_ids = torch.where(
last_valid_indices != -1, selected_tokens,
self.backup_next_token_ids.gpu[:batch_size])
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_draft_tokens_gpu = torch.cat([
spec_decode_metadata.cu_num_draft_tokens[0:1],
spec_decode_metadata.cu_num_draft_tokens[1:] -
spec_decode_metadata.cu_num_draft_tokens[:-1]
])
num_rejected_tokens_gpu = torch.where(
num_draft_tokens_gpu > 0,
num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
torch.zeros_like(num_draft_tokens_gpu))
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()
token_indices = self.arange[:total_num_tokens]
spec_common_attn_metadata = CommonAttentionMetadata(
query_start_loc=common_attn_metadata.query_start_loc,
seq_lens=common_attn_metadata.seq_lens,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
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.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,
)
token_indices_to_sample = common_attn_metadata.query_start_loc[1:] - 1 \
- num_rejected_tokens_gpu
return spec_common_attn_metadata, token_indices, token_indices_to_sample
def propose_tree(
self,
batch_size: int,
# [num_tokens, vocab_size]
logits: torch.Tensor,
# [num_tokens]
positions: torch.Tensor,
# [num_tokens, hidden_size]
hidden_states: torch.Tensor,
common_attn_metadata: CommonAttentionMetadata,
) -> list[torch.Tensor]:
tree_attn_metadata_builder = \
self.runner.attn_groups[0][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 layers.
per_layer_attn_metadata = {}
for layer_name in self.attn_layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
# Consider max model length.
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.
query_positions = flattened_draft_positions[:, level:level +
query_len]
block_numbers = query_positions // self.block_size
block_ids = attn_metadata.block_table.gather(dim=1,
index=block_numbers)
slot_mapping = (block_ids * self.block_size +
query_positions % self.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)
if self.use_cuda_graph and \
num_tokens <= self.cudagraph_batch_sizes[-1]:
num_input_tokens = self.vllm_config.pad_for_cudagraph(
num_tokens)
else:
num_input_tokens = num_tokens
# Run the model.
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens):
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_offests = 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_offests + 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,
)
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 load_model(self, target_model: nn.Module) -> None:
draft_model_config = \
self.vllm_config.speculative_config.draft_model_config
target_attn_layer_names = set(
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
# FIXME: support hybrid kv for draft model
target_indexer_layer_names = set(
get_layers_from_vllm_config(self.vllm_config,
DeepseekV32IndexerCache).keys())
from vllm.compilation.backends import set_model_tag
with set_model_tag("eagle_head"):
self.model = get_model(vllm_config=self.vllm_config,
model_config=draft_model_config)
draft_attn_layer_names = (
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
target_attn_layer_names)
indexer_layers = get_layers_from_vllm_config(self.vllm_config,
DeepseekV32IndexerCache)
draft_indexer_layer_names = (indexer_layers.keys() -
target_indexer_layer_names)
self.attn_layer_names = list(draft_attn_layer_names)
self.indexer_layer_names = list(draft_indexer_layer_names)
if self.indexer_layer_names:
first_layer = self.indexer_layer_names[0]
self.draft_indexer_metadata_builder = (
indexer_layers[first_layer].get_attn_backend().get_builder_cls(
)(
indexer_layers[first_layer].get_kv_cache_spec(),
self.indexer_layer_names,
self.vllm_config,
self.device,
))
else:
self.draft_indexer_metadata_builder = None
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.get_input_embeddings(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
if (self.get_model_name(target_model) ==
"Qwen2_5_VLForConditionalGeneration"):
self.model.config.image_token_index = (
target_model.config.image_token_id)
else:
self.model.config.image_token_index = (
target_model.config.image_token_index)
target_language_model = target_model.get_language_model()
else:
target_language_model = target_model
# share embed_tokens with the target model if needed
if get_pp_group().world_size == 1:
if hasattr(target_language_model.model, 'embed_tokens'):
target_embed_tokens = target_language_model.model.embed_tokens
elif hasattr(target_language_model.model, 'embedding'):
target_embed_tokens = target_language_model.model.embedding
else:
raise AttributeError(
"Target model does not have 'embed_tokens' or 'embedding' "
"attribute")
# Check if shapes match and we found the embedding
eagle_shape = self.model.model.embed_tokens.weight.shape
target_shape = target_embed_tokens.weight.shape
if eagle_shape == target_shape:
logger.info(
"Assuming the EAGLE head shares the same vocab embedding"
" with the target model.")
del self.model.model.embed_tokens
self.model.model.embed_tokens = target_embed_tokens
else:
logger.info(
"The EAGLE head's vocab embedding will be loaded separately"
" from the target model.")
else:
logger.info(
"The EAGLE head's vocab embedding will be loaded separately"
" from the target model.")
# share lm_head with the target model if needed
# some model definition do not define lm_head explicitly
# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
if self.vllm_config.speculative_config.method != "eagle3":
if hasattr(target_language_model, "lm_head"):
logger.info(
"Loading EAGLE LM head weights from the target model.")
self.model.lm_head = target_language_model.lm_head
else:
if (hasattr(self.model, "lm_head")
and hasattr(target_language_model, "lm_head")
and self.model.lm_head.weight.shape
== target_language_model.lm_head.weight.shape):
logger.info("Assuming the EAGLE head shares the same lm_head"
" with the target model.")
del self.model.lm_head
self.model.lm_head = target_language_model.lm_head
else:
logger.info(
"The EAGLE head's lm_head will be loaded separately"
" from the target model.")
@torch.inference_mode()
def dummy_run(
self,
num_tokens: int,
) -> None:
with set_forward_context(None, self.vllm_config,
num_tokens=num_tokens):
if self.supports_mm_inputs:
input_ids = None
inputs_embeds = self.inputs_embeds[:num_tokens]
else:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = None
self.model(
input_ids=input_ids,
positions=self._get_positions(num_tokens),
hidden_states=self.hidden_states[:num_tokens],
inputs_embeds=inputs_embeds,
)
def _get_attention_metadata_builder(
self) -> list[AttentionMetadataBuilder]:
"""Find and return the attention metadata builders for EAGLE layers.
Returns:
The metadata builders for EAGLE layers.
Raises:
AssertionError: If no metadata builders are found for EAGLE layers.
"""
builder = None
chosen_layer = self.attn_layer_names[0]
for kv_cache_group in self.runner.attn_groups:
for attn_group in kv_cache_group:
if chosen_layer in attn_group.layer_names:
builder = attn_group.get_metadata_builder()
break
if builder is not None:
break
assert builder is not None, (
"Failed to find attention metadata builder for EAGLE layers.")
return builder
def validate_same_kv_cache_group(self,
kv_cache_config: KVCacheConfig) -> None:
"""
Validate that all eagle layers belong to the same KVCacheGroup.
Need this assumption to ensure all eagle layers can use the
same AttentionMetadata.
May extend to multiple AttentionMetadata in the future.
"""
kv_cache_groups: dict[str, int] = {}
for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
for layer_name in kv_cache_group.layer_names:
kv_cache_groups[layer_name] = id
assert len(
set([
kv_cache_groups[layer_name]
for layer_name in self.attn_layer_names
])
) == 1, "All eagle layers should belong to the same kv cache group"