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luopingyi
2026-01-09 15:09:53 +08:00
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vllm/zero_overhead/v1/eagle.py Normal file
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import torch
from vllm.forward_context import set_forward_context
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
from vllm.v1.attention.backends.mla.common import MLACommonMetadata
from vllm.v1.attention.backends.utils import CommonAttentionMetadata
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID, EagleProposer
class V1ZeroEagleProposer(EagleProposer):
def __init__(self, vllm_config, device, runner=None):
super().__init__(vllm_config, device, runner)
self.spec_scheduler_max_num_tokens = 0
def propose(
self,
# [num_tokens]
target_token_ids: torch.Tensor,
# [num_tokens]
target_positions: torch.Tensor,
# [num_tokens, hidden_size]
target_hidden_states: torch.Tensor,
# [num_tokens]
target_slot_mapping: torch.Tensor,
# [batch_size]
next_token_ids: torch.Tensor,
# [batch_size + 1] starting with 0
cu_num_tokens: torch.Tensor,
# [batch_size, max_num_blocks_per_req]
block_table: torch.Tensor,
# [batch_size]
sampling_metadata: SamplingMetadata,
decoding: bool = False,
) -> torch.Tensor:
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = cu_num_tokens[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
# FA requires seq_len to have dtype int32.
seq_lens = (target_positions[last_token_indices] + 1).int()
if self.method in ["eagle", "eagle3"]:
# FIXME(woosuk): The below two ops cause synchronization. Optimize.
max_seq_len = seq_lens.max().item()
max_num_tokens = (cu_num_tokens[1:] -
cu_num_tokens[:-1]).max().item()
attn_metadata = FlashAttentionMetadata(
num_actual_tokens=num_tokens,
max_query_len=max_num_tokens,
query_start_loc=cu_num_tokens,
max_seq_len=max_seq_len,
seq_lens=seq_lens,
block_table=block_table,
slot_mapping=target_slot_mapping,
# TODO(woosuk): Support cascade attention.
use_cascade=False,
common_prefix_len=0,
cu_prefix_query_lens=None,
prefix_kv_lens=None,
suffix_kv_lens=None,
)
elif self.method == "deepseek_mtp":
max_query_len = self.spec_scheduler_max_num_tokens
common_attn_metadata = CommonAttentionMetadata(
query_start_loc=cu_num_tokens,
seq_lens=seq_lens,
num_reqs=batch_size,
num_actual_tokens=num_tokens,
max_query_len=max_query_len,
slot_mapping=target_slot_mapping,
spec_layer_decoding=decoding
)
assert self.runner is not None
# FIXME: need to consider multiple kv_cache_groups
attn_metadata = self.runner.attn_metadata_builders[0].build(
common_prefix_len=0,
common_attn_metadata=common_attn_metadata
)
else:
raise ValueError(f"Unsupported method: {self.method}")
# 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
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.positions[:num_tokens] = target_positions
self.hidden_states[:num_tokens] = target_hidden_states
if (decoding and self.use_full_cuda_graph
and num_tokens <= self.cudagraph_batch_sizes[-1]):
assert self.attn_metadata_cudagraph
if self.method in ["eagle", "eagle3"]:
self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
attn_metadata.seq_lens)
self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
attn_metadata.slot_mapping)
self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
attn_metadata.query_start_loc)
self.attn_metadata_cudagraph.block_table[:batch_size] = (
attn_metadata.block_table)
elif self.method == "deepseek_mtp":
self.attn_metadata_cudagraph.num_actual_tokens = (
attn_metadata.num_actual_tokens)
self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
attn_metadata.query_start_loc)
self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
attn_metadata.slot_mapping)
self.attn_metadata_cudagraph.num_decodes = (
attn_metadata.num_decodes)
self.attn_metadata_cudagraph.num_decode_tokens = (
attn_metadata.num_decode_tokens)
self.attn_metadata_cudagraph.num_prefills = (
attn_metadata.num_prefills)
if attn_metadata.decode is not None:
self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
attn_metadata.decode.block_table)
self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
attn_metadata.decode.seq_lens)
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
skip_cuda_graphs=not decoding):
ret_hidden_states = self.model(
self.input_ids[:num_input_tokens],
self.positions[:num_input_tokens],
self.hidden_states[:num_input_tokens],
)
if self.method == "deepseek_mtp":
last_hidden_states = ret_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, None)
draft_token_ids = logits.argmax(dim=-1)
# Early exit if there is only one draft token to be generated.
if self.num_speculative_tokens == 1:
# [batch_size, 1]
return draft_token_ids.view(-1, 1)
# TODO: Currently, MTP module released by deepseek only has
# one layer. Adapt this code to support multiple layers once
# there's a multi-layer MTP module.
# Generate the remaining draft tokens.
draft_token_ids_list = [draft_token_ids]
positions = target_positions[last_token_indices]
if self.method == "deepseek_mtp":
hidden_states = last_hidden_states[last_token_indices]
else:
hidden_states = hidden_states[last_token_indices]
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
attn_metadata.num_actual_tokens = batch_size
attn_metadata.max_query_len = 1
attn_metadata.query_start_loc = self.arange[:batch_size + 1]
if isinstance(attn_metadata, MLACommonMetadata):
attn_metadata.num_decodes = batch_size
attn_metadata.num_decode_tokens = batch_size
attn_metadata.num_prefills = 0
block_table = self.runner.attn_metadata_builders[0].block_table.get_device_tensor()[:batch_size, ...]
attn_metadata.decode = self.runner.attn_metadata_builders[0]._build_decode(
block_table_tensor=block_table,
seq_lens=seq_lens,
)
for i 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()
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 >= 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, 0,
positions)
if isinstance(attn_metadata, MLACommonMetadata):
attn_metadata.decode.seq_lens += 1
else:
attn_metadata.seq_lens += 1
# Increment the sequence lengths.
attn_metadata.max_seq_len += 1
# 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_numbers = clamped_positions // self.block_size
block_ids = block_table.gather(dim=1,
index=block_numbers.view(-1, 1))
block_ids = block_ids.view(-1)
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.
attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len,
PADDING_SLOT_ID)
# copy inputs to buffer for cudagraph
self.input_ids[:batch_size] = input_ids
self.positions[:batch_size] = clamped_positions
self.hidden_states[:batch_size] = hidden_states
if (self.use_full_cuda_graph
and batch_size <= self.cudagraph_batch_sizes[-1]):
assert self.attn_metadata_cudagraph
if self.method in ["eagle", "eagle3"]:
self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
attn_metadata.seq_lens)
self.attn_metadata_cudagraph.slot_mapping[:batch_size] = (
attn_metadata.slot_mapping)
if i == 0:
self.attn_metadata_cudagraph.query_start_loc[:batch_size +
1] = (
attn_metadata
.
query_start_loc
)
self.attn_metadata_cudagraph.block_table[:batch_size] = (
attn_metadata.block_table)
elif self.method == "deepseek_mtp":
self.attn_metadata_cudagraph.num_actual_tokens = (
attn_metadata.num_actual_tokens)
self.attn_metadata_cudagraph.slot_mapping[:attn_metadata.num_decode_tokens] = (
attn_metadata.slot_mapping)
self.attn_metadata_cudagraph.num_decodes = (
attn_metadata.num_decodes)
self.attn_metadata_cudagraph.num_decode_tokens = (
attn_metadata.num_decode_tokens)
self.attn_metadata_cudagraph.num_prefills = (
attn_metadata.num_prefills)
self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
attn_metadata.decode.seq_lens)
if i == 0:
self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
attn_metadata.query_start_loc)
self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
attn_metadata.decode.block_table)
# Run the model.
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=input_batch_size):
ret_hidden_states = self.model(
self.input_ids[:input_batch_size],
self.positions[:input_batch_size],
self.hidden_states[:input_batch_size],
)
if self.method == "deepseek_mtp":
last_hidden_states = ret_hidden_states
hidden_states = last_hidden_states[:batch_size]
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],
None)
# TODO(wenlong): get more than one token for tree attention
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