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[Refactor] Refactor Spec Decode (#2668)

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### What this PR does / why we need it?
Refactor spec decode

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.


- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Icey <1790571317@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
Icey
2025-09-04 11:34:47 +08:00
committed by GitHub
parent 7e16b4a7cd
commit d4370ebc42
7 changed files with 1021 additions and 932 deletions
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33
vllm_ascend/spec_decode/__init__.py Normal file
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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
#
from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
from vllm_ascend.spec_decode.ngram_proposer import NgramProposer
def get_spec_decode_method(method, vllm_config, device, runner):
if method == "ngram":
return NgramProposer(vllm_config, device, runner)
elif method in ["eagle", "eagle3"]:
return EagleProposer(vllm_config, device, runner)
elif method == 'deepseek_mtp':
return MtpProposer(vllm_config, device, runner)
else:
raise ValueError("Unknown speculative decoding method: "
f"{method}")

642
vllm_ascend/spec_decode/eagle_proposer.py Normal file
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# SPDX-License-Identifier: Apache-2.0
import os
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from vllm.attention.layer import Attention
from vllm.config import (CompilationLevel, VllmConfig,
get_layers_from_vllm_config)
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import logger
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models import supports_multimodal
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
PADDING_SLOT_ID = -1
class EagleProposer(Proposer):
def __init__(self,
vllm_config: VllmConfig,
device: torch.device,
runner=None):
self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3
self.vllm_config = vllm_config
self.device = device
self.runner = runner
self.block_size = vllm_config.cache_config.block_size
# 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 = vllm_config.speculative_config.draft_model_config.get_hidden_size(
)
self.use_cuda_graph = (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))
# persistent buffers for cuda graph
self.input_ids = torch.zeros(
self.vllm_config.scheduler_config.max_num_batched_tokens,
dtype=torch.int32,
device=device)
self.positions = torch.zeros(
self.vllm_config.scheduler_config.max_num_batched_tokens,
dtype=torch.int64,
device=device)
self.hidden_states = torch.zeros(
(self.vllm_config.scheduler_config.max_num_batched_tokens,
self.hidden_size),
dtype=self.vllm_config.model_config.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.
self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
1,
device=device,
dtype=torch.int32)
attn_mask_len = min(self.vllm_config.model_config.max_model_len,
int(os.getenv("PAGED_ATTENTION_MASK_LEN", 10000)))
self.attn_mask_builder = AttentionMaskBuilder(
attn_mask_len, self.vllm_config.model_config.dtype)
def load_model(self, model: nn.Module) -> None:
target_attn_layer_names = set(
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
self.model = get_model(vllm_config=self.vllm_config,
model_config=self.vllm_config.
speculative_config.draft_model_config)
draft_attn_layer_names = (
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
target_attn_layer_names)
self.attn_layer_name = next(iter(draft_attn_layer_names))
# share embed_tokens with the target model if needed
if get_pp_group().world_size == 1:
logger.info(
"The EAGLE head shares the same vocab embedding" \
" with the target model."
)
self.model.model.embed_tokens = model.model.embed_tokens
else:
logger.info(
"Since PP > 1, the EAGLE head loaded its own vocab embedding" \
" weights instead of sharing them with 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.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
logger.info("Loading EAGLE LM head weights from the target model.")
if supports_multimodal(model):
self.model.lm_head = model.get_language_model().lm_head
else:
self.model.lm_head = model.lm_head
@torch.inference_mode()
def dummy_run(self,
num_tokens: int,
with_prefill: bool = False,
skip_attn: bool = False,
num_reqs: int = 0,
num_tokens_across_dp: Optional[torch.Tensor] = None):
with set_ascend_forward_context(None,
self.vllm_config,
num_tokens=num_tokens):
self.model(
input_ids=self.input_ids[:num_tokens],
positions=self.positions[:num_tokens],
hidden_states=self.hidden_states[:num_tokens],
)
def generate_token_ids(self,
valid_sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata = None,
scheduler_output: SchedulerOutput = None,
spec_decode_metadata: SpecDecodeMetadata = None,
positions: torch.Tensor = None,
num_scheduled_tokens: int = 0,
hidden_states: torch.Tensor = None,
attn_metadata=None,
aux_hidden_states: torch.Tensor = None):
if self.name == SpecDcodeType.EAGLE:
raise NotImplementedError("Eagle Is Not Supported Yet.")
attn_metadata = self._get_eagle_atten_dict(scheduler_output)
next_token_ids: list[int] = []
for i, token_ids in enumerate(valid_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 = self.runner.input_batch.req_ids[i]
req_state = self.runner.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.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.device)
eagle_attn_metadata = attn_metadata[self.attn_layer_name]
if spec_decode_metadata is None:
# input_ids can be None for multimodal models.
target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
target_positions = positions[:num_scheduled_tokens]
if self.name == SpecDcodeType.EAGLE3:
target_hidden_states = torch.cat(
[h[:num_scheduled_tokens] for h in aux_hidden_states],
dim=-1)
else:
target_hidden_states = hidden_states[:num_scheduled_tokens]
target_slot_mapping = eagle_attn_metadata.slot_mapping
cu_num_tokens = eagle_attn_metadata.query_start_loc
else:
num_draft_tokens = spec_decode_metadata.num_draft_tokens
num_rejected_tokens = [
n + 1 - len(valid_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=self.device,
)
num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
cu_num_tokens, token_indices = self._prepare_inputs(
eagle_attn_metadata.query_start_loc, num_rejected_tokens,
num_tokens)
target_token_ids = self.runner.input_ids[token_indices]
target_positions = positions[token_indices]
if self.name == SpecDcodeType.EAGLE3:
target_hidden_states = torch.cat(
[h[token_indices] for h in aux_hidden_states], dim=-1)
else:
target_hidden_states = hidden_states[token_indices]
target_slot_mapping = eagle_attn_metadata.slot_mapping[
token_indices]
draft_token_ids = self._propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
target_slot_mapping=target_slot_mapping,
next_token_ids=next_token_ids,
cu_num_tokens=cu_num_tokens,
block_table=eagle_attn_metadata.block_tables,
sampling_metadata=sampling_metadata,
)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids
def _get_eagle_atten_dict(
self,
scheduler_output: "SchedulerOutput",
):
total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
assert total_num_scheduled_tokens > 0
num_reqs = self.runner.input_batch.num_reqs
assert num_reqs > 0
# OPTIMIZATION: Start copying the block table first.
# This way, we can overlap the copy with the following CPU operations.
self.runner.input_batch.block_table.commit_block_table(num_reqs)
# Get the number of scheduled tokens for each request.
req_ids = self.runner.input_batch.req_ids
tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
num_scheduled_tokens = np.array(tokens, dtype=np.int32)
max_num_scheduled_tokens = max(tokens)
self.runner.query_lens = torch.from_numpy(num_scheduled_tokens)
# Get request indices.
# E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
req_indices = np.repeat(self.runner.arange_np[:num_reqs],
num_scheduled_tokens)
# cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
# arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
cu_num_tokens, arange = self._get_cumsum_and_arange(
num_scheduled_tokens)
# Get positions.
positions_np = self.runner.positions_np[:total_num_scheduled_tokens]
np.add(self.runner.input_batch.num_computed_tokens_cpu[req_indices],
arange,
out=positions_np)
# Calculate M-RoPE positions.
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.runner.uses_mrope:
self.runner._calc_mrope_positions(scheduler_output)
# Get token indices.
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
# where M is the max_model_len.
token_indices = (
positions_np +
req_indices * self.runner.input_batch.token_ids_cpu.shape[1])
# NOTE(woosuk): We use torch.index_select instead of np.take here
# because torch.index_select is much faster than np.take for large
# tensors.
torch.index_select(
self.runner.input_batch.token_ids_cpu_tensor.flatten(),
0,
torch.from_numpy(token_indices),
out=self.runner.input_ids_cpu[:total_num_scheduled_tokens])
# Prepare the attention metadata for each KV cache group and make layers
# in the same group share the same metadata.
# NOTE(Chen): there is exactly one KV cache group that contains all
# attetnion layers in the model for now, so the current logic for
# getting attn_metadata is not related to kv_cache_group information.
# Will extend this part to support multiple KV cache groups later.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.runner.kv_cache_config.kv_cache_groups):
block_size = kv_cache_group_spec.kv_cache_spec.block_size
block_table = self.runner.input_batch.block_table[
kv_cache_group_id]
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
# where K is the max_num_blocks_per_req and the block size is 2.
# NOTE(woosuk): We can't simply use `token_indices // block_size`
# here because M (max_model_len) is not necessarily divisible by
# block_size.
block_table_indices = (
req_indices * block_table.max_num_blocks_per_req +
positions_np // block_size)
block_table_cpu = block_table.get_cpu_tensor()
block_numbers = block_table_cpu.flatten(
)[block_table_indices].numpy()
block_offsets = positions_np % block_size
np.add(
block_numbers * block_size,
block_offsets,
out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
# Prepare the attention metadata.
self.runner.query_start_loc_np[0] = 0
self.runner.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
self.runner.seq_lens_np[:num_reqs] = (
self.runner.input_batch.num_computed_tokens_cpu[:num_reqs] +
num_scheduled_tokens)
# Copy the tensors to the NPU.
self.runner.input_ids[:total_num_scheduled_tokens].copy_(
self.runner.input_ids_cpu[:total_num_scheduled_tokens],
non_blocking=True)
if self.runner.uses_mrope:
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
self.runner.mrope_positions[:, :total_num_scheduled_tokens].copy_(
self.runner.
mrope_positions_cpu[:, :total_num_scheduled_tokens],
non_blocking=True)
else:
# Common case (1D positions)
self.runner.positions[:total_num_scheduled_tokens].copy_(
self.runner.positions_cpu[:total_num_scheduled_tokens],
non_blocking=True)
self.runner.query_start_loc[:num_reqs + 1].copy_(
self.runner.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
self.runner.seq_lens[:num_reqs].copy_(
self.runner.seq_lens_cpu[:num_reqs], non_blocking=True)
# Fill unused with -1. Needed for reshape_and_cache
self.runner.seq_lens[num_reqs:].fill_(0)
self.runner.query_start_loc[num_reqs + 1:].fill_(-1)
attn_metadata = {}
# Prepare the attention metadata for each KV cache group and make layers
# in the same group share the same metadata.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.runner.kv_cache_config.kv_cache_groups):
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=self.runner.query_start_loc[:num_reqs + 1],
query_start_loc_cpu=self.runner.query_start_loc_cpu[:num_reqs +
1],
seq_lens_cpu=self.runner.seq_lens_cpu,
num_reqs=num_reqs,
max_query_len=max_num_scheduled_tokens,
num_actual_tokens=total_num_scheduled_tokens,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
block_table_tensor=self.runner.input_batch.block_table[0].
get_device_tensor(),
slot_mapping_cpu=self.runner.slot_mapping_cpu,
positions=self.runner.positions,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
decode_token_per_req=self.runner.decode_token_per_req,
)
attn_metadata_i = self.runner.attn_metadata_builder.build(
common_attn_metadata, self.runner.get_model())
for layer_name in kv_cache_group_spec.layer_names:
attn_metadata[layer_name] = attn_metadata_i
return attn_metadata
def _get_cumsum_and_arange(
self,
num_tokens: np.ndarray,
cumsum_dtype: Optional[np.dtype] = None,
) -> tuple[np.ndarray, np.ndarray]:
"""Get the cumulative sum and batched arange of the given array.
# E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
# Equivalent to but faster than:
# np.concatenate([np.arange(n) for n in num_tokens])
"""
# Step 1. [2, 5, 3] -> [2, 7, 10]
cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
total_num_tokens = cu_num_tokens[-1]
# Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
# Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
arange = self.runner.arange_np[:total_num_tokens] - cumsums_offsets
return cu_num_tokens, arange
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,
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
device = cu_num_tokens.device
cu_num_tokens = cu_num_tokens.cpu()
block_table = block_table.cpu()
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = cu_num_tokens[1:] - 1
target_positions = target_positions.cpu()
if self.name == SpecDcodeType.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[0]
query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
max_query_len = query_lens.max().item()
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=self.runner.query_start_loc[:batch_size + 1],
query_start_loc_cpu=self.runner.query_start_loc_cpu[:batch_size +
1],
seq_lens_cpu=self.runner.seq_lens_cpu,
max_query_len=max_query_len,
num_reqs=batch_size,
num_actual_tokens=num_tokens,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
block_table_tensor=self.runner.input_batch.block_table[0].
get_device_tensor(),
slot_mapping_cpu=target_slot_mapping,
positions=target_positions,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
decode_token_per_req=self.runner.decode_token_per_req,
)
# FIXME(woosuk): The below two ops cause synchronization. Optimize.
attn_metadata = self.runner.attn_metadata_builder.build(
common_attn_metadata, self.runner.model)
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.to(device)
self.hidden_states[:num_tokens] = target_hidden_states
attn_metadata.block_tables = block_table.to(device)
with set_ascend_forward_context(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],
)
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.vllm_config.speculative_config.num_speculative_tokens == 1:
# [batch_size, 1]
return draft_token_ids.view(-1, 1)
# Generate the remaining draft tokens.
draft_token_ids_tensor = torch.zeros(
(self.vllm_config.speculative_config.num_speculative_tokens,
*draft_token_ids.shape),
dtype=draft_token_ids.dtype)
draft_token_ids_tensor[0] = draft_token_ids
positions_cpu = target_positions[last_token_indices].cpu().to(
torch.int64)
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 self.vllm_config.speculative_config.num_speculative_tokens > 2:
raise ValueError("Speculative tokens > 2 are not supported yet.")
attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
for now_speculative in range(
self.vllm_config.speculative_config.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_tensor[now_speculative].to(device)
positions_cpu += 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_cpu >= self.vllm_config.model_config.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_cpu = torch.where(exceeds_max_model_len, 0,
positions_cpu)
clamped_positions = clamped_positions_cpu.to(device)
# TODO: Increment the sequence lengths.
attn_metadata.seq_lens += 1
# TODO: 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
# TODO: sequence length to 1 to minimize their overheads in attention.
# Compute the slot mapping.
block_numbers = (clamped_positions_cpu // self.block_size)
block_ids = block_table.gather(dim=1,
index=block_numbers.view(-1, 1))
block_ids = block_ids.view(-1)
slot_mapping_cpu = (
block_ids * self.vllm_config.cache_config.block_size +
clamped_positions_cpu % 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_cpu.masked_fill_(exceeds_max_model_len,
PADDING_SLOT_ID)
# NOTE: ASCEND slot_mapping must on cpu
attn_metadata.slot_mapping = slot_mapping_cpu.to(
torch.int32).to(device)
# 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
positions = positions_cpu.to(device)
attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
attn_metadata.seq_lens, positions,
self.vllm_config.model_config.dtype, self.device)
attn_metadata.attn_mask = attn_mask
attn_metadata.block_tables = block_table.to(device)
# Run the model.
with set_ascend_forward_context(attn_metadata,
self.vllm_config,
num_tokens=input_batch_size):
last_hidden_states, hidden_states = self.model(
input_ids=self.input_ids[:input_batch_size],
positions=self.positions[:input_batch_size],
hidden_states=self.hidden_states[:input_batch_size],
)
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_tensor[now_speculative + 1] = draft_token_ids.cpu()
# [batch_size, num_speculative_tokens]
draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
return draft_token_ids
def _prepare_inputs(
self,
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
num_rejected_tokens: torch.Tensor,
num_tokens: int,
) -> tuple[torch.Tensor, torch.Tensor]:
# cu_target_query_lens: [0, a, a + b, a + b + c]
# num_rejected_tokens: [n1, n2, n3]
# num_tokens_per_req: [a - n1, b - n2, c - n3]
# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
# token_indices: [0, 1, ..., a - n1 - 1,
# a, a + 1, ..., a + b - n2 - 1,
# a + b, a + b + 1, ..., a + b + c - n3 - 1]
# [0, a, a + b, a + b + c] -> [a, b, c]
query_len_per_req = (cu_target_query_lens[1:] -
cu_target_query_lens[:-1])
# [a, b, c] -> [a - n1, b - n2, c - n3]
num_tokens_per_req = query_len_per_req - num_rejected_tokens
# [a - n1, b - n2, c - n3] ->
# [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
cu_num_tokens = torch.zeros_like(cu_target_query_lens)
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
token_indices = torch.empty(
num_tokens,
dtype=torch.int32,
device=cu_target_query_lens.device,
)
BLOCK_SIZE = 1024
self._prepare_eagle_input_sequential(
token_indices,
cu_target_query_lens,
cu_num_tokens,
block_size=BLOCK_SIZE,
)
return cu_num_tokens, token_indices
def _prepare_eagle_input_sequential(self, out_tensor: torch.Tensor,
cu_query_lens: torch.Tensor,
cu_num_tokens: torch.Tensor,
block_size: int):
num_programs = len(cu_num_tokens) - 1
for pid in range(num_programs):
start_pos = cu_num_tokens[pid].item()
end_pos = cu_num_tokens[pid + 1].item()
num_tokens = end_pos - start_pos
index_start = cu_query_lens[pid].item()
num_blocks = int(
torch.ceil(torch.tensor(num_tokens / block_size)).item())
for i in range(num_blocks):
offset_tensor = torch.arange(0,
block_size,
dtype=torch.int32,
device=out_tensor.device)
global_start_offset = i * block_size
target_indices = torch.tensor(
start_pos + global_start_offset,
dtype=torch.int32,
device=out_tensor.device) + offset_tensor
values_to_store = torch.tensor(
index_start, dtype=torch.int32,
device=out_tensor.device) + offset_tensor
mask = (target_indices >= start_pos) & \
(target_indices < end_pos) & \
(offset_tensor < num_tokens)
out_tensor[target_indices[mask]] = values_to_store[mask]

51
vllm_ascend/spec_decode/interface.py Normal file
View File

@@ -0,0 +1,51 @@
import enum
from typing import Optional
import torch
from vllm.config import VllmConfig
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
class SpecDcodeType(enum.Enum):
NGRAM = 0
EAGLE = 1
EAGLE3 = 2
MTP = 4
class Proposer:
def __init__(self,
vllm_config: VllmConfig,
device: torch.device = None,
runner=None):
pass
def load_model(self, model):
"""Called by load_model in model_runner"""
raise NotImplementedError
@torch.inference_mode()
def dummy_run(self,
num_tokens: int,
with_prefill: bool = False,
skip_attn: bool = False,
num_reqs: int = 0,
num_tokens_across_dp: Optional[torch.Tensor] = None):
"""Called by dummy_run in modle_runner"""
raise NotImplementedError
def generate_token_ids(self,
valid_sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata = None,
scheduler_output: SchedulerOutput = None,
spec_decode_metadata: SpecDecodeMetadata = None,
positions: torch.Tensor = None,
num_scheduled_tokens: int = 0,
hidden_states: torch.Tensor = None,
attn_metadata=None,
aux_hidden_states: torch.Tensor = None):
"""Called by execute_model in model_runner"""
raise NotImplementedError

343
vllm_ascend/worker/mtp_proposer_v1.py → vllm_ascend/spec_decode/mtp_proposer.py
View File

@@ -12,12 +12,15 @@ from vllm.forward_context import get_forward_context
from vllm.model_executor.model_loader import get_model_loader
from vllm.model_executor.model_loader.utils import (
process_weights_after_loading, set_default_torch_dtype)
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.models.deepseek_mtp import CustomDeepSeekMTP
from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
from vllm_ascend.torchair.models.torchair_deepseek_mtp import \
TorchairDeepSeekMTP
from vllm_ascend.torchair.utils import (TORCHAIR_CACHE_DIR,
@@ -25,37 +28,214 @@ from vllm_ascend.torchair.utils import (TORCHAIR_CACHE_DIR,
from vllm_ascend.utils import ProfileExecuteDuration, lmhead_tp_enable
class MtpProposer:
class MtpProposer(Proposer):
def __init__(
self,
vllm_config: VllmConfig,
device,
runner,
):
self.name = SpecDcodeType.MTP
self.vllm_config = vllm_config
self.num_speculative_tokens = (
vllm_config.speculative_config.num_speculative_tokens)
self.block_size = vllm_config.cache_config.block_size
self.hidden_size = vllm_config.model_config.get_hidden_size()
self.device = device
self.runner = runner
# persistent buffers for graph
self.input_ids = torch.zeros(self.runner.max_num_tokens,
dtype=torch.int32,
device=self.runner.device)
device=self.device)
self.positions = torch.zeros(self.runner.max_num_tokens,
dtype=torch.int64,
device=self.runner.device)
device=self.device)
self.hidden_states = torch.zeros(
(self.runner.max_num_tokens, self.hidden_size),
(self.runner.max_num_tokens,
vllm_config.model_config.get_hidden_size()),
dtype=self.runner.dtype,
device=self.runner.device)
device=self.device)
self.torchair_compiled_model = None # type: ignore
self.torchair_compiled_models = {} # type: ignore
self.torchair_graph_enabled = get_ascend_config(
).torchair_graph_config.enabled
@staticmethod
def prepare_inputs(
def load_model(self, model) -> None:
loader = get_model_loader(self.vllm_config.load_config)
target_attn_layer_names = set(
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
draft_model_config = \
self.vllm_config.speculative_config.draft_model_config
target_device = self.vllm_config.device_config.device
with set_default_torch_dtype(
draft_model_config.dtype), set_current_vllm_config(
self.vllm_config):
if self.torchair_graph_enabled:
self.model = TorchairDeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
else:
self.model = CustomDeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
draft_attn_layer_names = (
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
target_attn_layer_names)
assert len(draft_attn_layer_names) == 1
self.attn_layer_name = next(iter(draft_attn_layer_names))
self.model.load_weights(
loader.get_all_weights(
self.vllm_config.speculative_config.draft_model_config,
self.model))
process_weights_after_loading(self.model, draft_model_config,
target_device)
@torch.inference_mode()
def dummy_run(self,
num_tokens: int,
with_prefill: bool = False,
skip_attn: bool = False,
num_reqs: int = 0,
num_tokens_across_dp=None) -> None:
if not self.torchair_graph_enabled:
# TODO: adapt enable_dbo later
(num_tokens, num_tokens_across_dp, with_prefill,
_) = self.runner._sync_metadata_across_dp(num_tokens,
with_prefill, False)
is_running_torchair = self.torchair_graph_enabled and \
not with_prefill
if is_running_torchair:
skip_attn = False
if skip_attn:
attn_metadata = None
else:
common_attn_metadata = TorchairCommonAttentionMetadata(
num_reqs=num_reqs,
num_actual_tokens=1,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
decode_token_per_req=self.runner.decode_token_per_req,
)
attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
common_attn_metadata)
input_ids = self.input_ids[:num_tokens]
positions = self.positions[:num_tokens]
previous_hidden_states = self.hidden_states[:num_tokens]
with set_ascend_forward_context(
attn_metadata,
self.vllm_config,
num_tokens=num_tokens,
with_prefill=with_prefill,
num_tokens_across_dp=num_tokens_across_dp,
reserved_mc2_mask=self.runner.reserved_mc2_mask,
in_profile_run=self.runner.in_profile_run,
num_actual_tokens=0):
if is_running_torchair:
assert attn_metadata is not None
torch._dynamo.mark_static(input_ids)
torch._dynamo.mark_static(positions)
torch._dynamo.mark_static(previous_hidden_states)
torch._dynamo.mark_static(attn_metadata.decode.block_table)
torch._dynamo.mark_static(attn_metadata.decode.input_positions)
if hasattr(attn_metadata.decode, "sin"):
torch._dynamo.mark_static(attn_metadata.decode.sin)
torch._dynamo.mark_static(attn_metadata.decode.cos)
torch._dynamo.mark_static(get_forward_context().mc2_mask)
torch._dynamo.mark_static(attn_metadata.slot_mapping)
torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
torchair_compiled_model = self._get_torchair_lazy_compiled_model(
num_tokens)
torchair_compiled_model(
input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states,
inputs_embeds=None,
intermediate_tensors=None,
attn_metadata=attn_metadata,
kv_caches=self.runner.kv_caches[-1:],
spec_step_idx=0)
else:
self.model(input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states)
def generate_token_ids(self,
valid_sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata = None,
scheduler_output: SchedulerOutput = None,
spec_decode_metadata: SpecDecodeMetadata = None,
positions: torch.Tensor = None,
num_scheduled_tokens: int = 0,
hidden_states: torch.Tensor = None,
attn_metadata=None,
aux_hidden_states: torch.Tensor = None):
next_token_ids: list[int] = []
for i, token_ids in enumerate(valid_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 = self.runner.input_batch.req_ids[i]
req_state = self.runner.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.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.device)
accepted_token_indices = None
if spec_decode_metadata is None:
# input_ids can be None for multimodal models.
target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
target_positions = positions[:num_scheduled_tokens]
target_hidden_states = hidden_states[:num_scheduled_tokens]
target_slot_mapping = attn_metadata.slot_mapping
cu_num_tokens = attn_metadata.query_start_loc
else:
# TODO(woosuk): Refactor this.
num_draft_tokens = spec_decode_metadata.num_draft_tokens
num_rejected_tokens = [
n + 1 - len(valid_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=self.device,
)
cu_num_tokens, accepted_token_indices, target_token_ids, \
target_positions, target_hidden_states, target_slot_mapping = self._prepare_inputs(
attn_metadata.query_start_loc,
num_rejected_tokens,
self.runner.input_ids[:num_scheduled_tokens],
positions[:num_scheduled_tokens],
hidden_states[:num_scheduled_tokens],
attn_metadata.slot_mapping[:num_scheduled_tokens],
is_torchair_graph=self.runner._build_drafter_prepare_inputs_torchair_param(),
)
draft_token_ids = self._propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
target_slot_mapping=target_slot_mapping,
next_token_ids=next_token_ids,
cu_num_tokens=cu_num_tokens,
block_table=attn_metadata.block_tables,
sampling_metadata=sampling_metadata,
token_indices=accepted_token_indices)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids
def _prepare_inputs(
self,
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
@@ -102,7 +282,7 @@ class MtpProposer:
)
BLOCK_SIZE = 1024
prepare_input_kernel(
self._prepare_input_kernel(
token_indices,
cu_target_query_lens,
cu_num_tokens,
@@ -114,7 +294,7 @@ class MtpProposer:
target_slot_mapping = slot_mapping[token_indices]
return cu_num_tokens, token_indices, target_token_ids, target_positions, target_hidden_states, target_slot_mapping
def propose(
def _propose(
self,
# [num_tokens]
target_token_ids: torch.Tensor,
@@ -257,111 +437,6 @@ class MtpProposer:
# [batch_size, 1]
return draft_token_ids.view(-1, 1)
def load_model(self) -> None:
loader = get_model_loader(self.vllm_config.load_config)
target_attn_layer_names = set(
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
draft_model_config = \
self.vllm_config.speculative_config.draft_model_config
target_device = self.vllm_config.device_config.device
with set_default_torch_dtype(
draft_model_config.dtype), set_current_vllm_config(
self.vllm_config):
if self.torchair_graph_enabled:
self.model = TorchairDeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
else:
self.model = CustomDeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
draft_attn_layer_names = (
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
target_attn_layer_names)
assert len(draft_attn_layer_names) == 1
self.attn_layer_name = next(iter(draft_attn_layer_names))
self.model.load_weights(
loader.get_all_weights(
self.vllm_config.speculative_config.draft_model_config,
self.model))
process_weights_after_loading(self.model, draft_model_config,
target_device)
@torch.inference_mode()
def dummy_run(self,
num_tokens: int,
with_prefill: bool = False,
skip_attn: bool = False,
num_reqs: int = 0,
num_tokens_across_dp=None) -> None:
if not self.torchair_graph_enabled:
# TODO: adapt enable_dbo later
(num_tokens, num_tokens_across_dp, with_prefill,
_) = self.runner._sync_metadata_across_dp(num_tokens,
with_prefill, False)
is_running_torchair = self.torchair_graph_enabled and \
not with_prefill
if is_running_torchair:
skip_attn = False
if skip_attn:
attn_metadata = None
else:
common_attn_metadata = TorchairCommonAttentionMetadata(
num_reqs=num_reqs,
num_actual_tokens=1,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
decode_token_per_req=self.runner.decode_token_per_req,
)
attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
common_attn_metadata)
input_ids = self.input_ids[:num_tokens]
positions = self.positions[:num_tokens]
previous_hidden_states = self.hidden_states[:num_tokens]
with set_ascend_forward_context(
attn_metadata,
self.vllm_config,
num_tokens=num_tokens,
with_prefill=with_prefill,
num_tokens_across_dp=num_tokens_across_dp,
reserved_mc2_mask=self.runner.reserved_mc2_mask,
in_profile_run=self.runner.in_profile_run,
num_actual_tokens=0):
if is_running_torchair:
assert attn_metadata is not None
torch._dynamo.mark_static(input_ids)
torch._dynamo.mark_static(positions)
torch._dynamo.mark_static(previous_hidden_states)
torch._dynamo.mark_static(attn_metadata.decode.block_table)
torch._dynamo.mark_static(attn_metadata.decode.input_positions)
if hasattr(attn_metadata.decode, "sin"):
torch._dynamo.mark_static(attn_metadata.decode.sin)
torch._dynamo.mark_static(attn_metadata.decode.cos)
torch._dynamo.mark_static(get_forward_context().mc2_mask)
torch._dynamo.mark_static(attn_metadata.slot_mapping)
torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
torchair_compiled_model = self._get_torchair_lazy_compiled_model(
num_tokens)
torchair_compiled_model(
input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states,
inputs_embeds=None,
intermediate_tensors=None,
attn_metadata=attn_metadata,
kv_caches=self.runner.kv_caches[-1:],
spec_step_idx=0)
else:
self.model(input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states)
def _get_torchair_lazy_compiled_model(self, batch_size: int):
if batch_size < 0 or batch_size > self.runner.torchair_graph_batch_sizes[
-1]:
@@ -417,23 +492,23 @@ class MtpProposer:
ge_cache=False)
return self.torchair_compiled_models[batch_size]
# TODO Using torch instead of triton may result in poor performance
def _prepare_input_kernel(self, out_ptr: torch.Tensor,
cu_query_lens: torch.Tensor,
cu_num_tokens: torch.Tensor, block_size: int):
device = cu_query_lens.device
dtype = out_ptr.dtype
# TODO Using torch instead of triton may result in poor performance
def prepare_input_kernel(out_ptr: torch.Tensor, cu_query_lens: torch.Tensor,
cu_num_tokens: torch.Tensor, block_size: int):
device = cu_query_lens.device
dtype = out_ptr.dtype
offsets = torch.arange(block_size, device=device, dtype=dtype)
start_pos = cu_num_tokens[:-1]
end_pos = cu_num_tokens[1:]
num_tokens = end_pos - start_pos
offsets = torch.arange(block_size, device=device, dtype=dtype)
start_pos = cu_num_tokens[:-1]
end_pos = cu_num_tokens[1:]
num_tokens = end_pos - start_pos
global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
global_indices_flat = global_indices[mask]
values_flat = values[mask]
out_ptr[global_indices_flat] = values_flat
global_indices_flat = global_indices[mask]
values_flat = values[mask]
out_ptr[global_indices_flat] = values_flat

65
vllm_ascend/spec_decode/ngram_proposer.py Normal file
View File

@@ -0,0 +1,65 @@
import torch
from vllm.v1.spec_decode.ngram_proposer import \
NgramProposer as VllmNgramProposer
from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
class NgramProposer(VllmNgramProposer, Proposer):
def __init__(self, vllm_config, device, runner):
super().__init__(vllm_config)
self.name = SpecDcodeType.NGRAM
self.device = device
self.runner = runner
def load_model(self, *args, **kwargs):
# No model to load.
pass
@torch.inference_mode()
def dummy_run(self,
num_tokens,
with_prefill=None,
skip_attn=None,
num_reqs=None,
num_tokens_across_dp=None):
pass
def generate_token_ids(self,
valid_sampled_token_ids,
sampling_metadata=None,
scheduler_output=None,
spec_decode_metadata=None,
positions=None,
num_scheduled_tokens=None,
hidden_states=None,
attn_metadata=None,
aux_hidden_states=None) -> list[list[int]]:
# TODO(woosuk): Optimize.
draft_token_ids: list[list[int]] = []
for i, sampled_ids in enumerate(valid_sampled_token_ids):
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
draft_token_ids.append([])
continue
# Skip requests that require top-p, top-k, etc.
req_id = self.runner.input_batch.req_ids[i]
if req_id in self.runner.input_batch.spec_decode_unsupported_reqs:
draft_token_ids.append([])
continue
# Add sampled_token_ids to token_ids_cpu.
start_idx = self.runner.input_batch.num_tokens_no_spec[i]
end_idx = start_idx + num_sampled_ids
self.runner.input_batch.token_ids_cpu[
i, start_idx:end_idx] = sampled_ids
drafter_output = self.propose(
self.runner.input_batch.token_ids_cpu[i, :end_idx])
if drafter_output is None or len(drafter_output) == 0:
draft_token_ids.append([])
else:
draft_token_ids.append(drafter_output.tolist())
return draft_token_ids

398
vllm_ascend/worker/eagle_proposer_v1.py
View File

@@ -1,398 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
import os
import torch
import torch.nn as nn
from vllm.attention.layer import Attention
from vllm.config import (CompilationLevel, VllmConfig,
get_layers_from_vllm_config)
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import logger
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models import supports_multimodal
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.sample.metadata import SamplingMetadata
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
PADDING_SLOT_ID = -1
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.model_config = vllm_config.model_config
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.device = device
# 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.use_cuda_graph = (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))
# persistent buffers for cuda graph
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device=device)
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.
self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
1,
device=device,
dtype=torch.int32)
mask_len = os.getenv("PAGED_ATTENTION_MASK_LEN", 10000)
self.attn_mask_len = min(self.model_config.max_model_len,
int(mask_len))
self.attn_mask_builder = AttentionMaskBuilder(self.attn_mask_len,
self.dtype)
def _make_attention_mask(
self,
seq_lens,
position,
) -> torch.Tensor:
return self.attn_mask_builder.get_splitfuse_attn_mask(
seq_lens, position, self.dtype, self.device)
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,
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
device = cu_num_tokens.device
cu_num_tokens = cu_num_tokens.cpu()
block_table = block_table.cpu()
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = cu_num_tokens[1:] - 1
target_positions = target_positions.cpu()
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[0]
query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
max_query_len = query_lens.max().item()
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=self.runner.query_start_loc[:batch_size + 1],
query_start_loc_cpu=self.runner.query_start_loc_cpu[:batch_size +
1],
seq_lens_cpu=self.runner.seq_lens_cpu,
max_query_len=max_query_len,
num_reqs=batch_size,
num_actual_tokens=num_tokens,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
block_table_tensor=self.runner.input_batch.block_table[0].
get_device_tensor(),
slot_mapping_cpu=target_slot_mapping,
positions=target_positions,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
decode_token_per_req=self.runner.decode_token_per_req,
)
# FIXME(woosuk): The below two ops cause synchronization. Optimize.
attn_metadata = self.runner.attn_metadata_builder.build(
common_attn_metadata, self.runner.model)
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.to(device)
self.hidden_states[:num_tokens] = target_hidden_states
attn_metadata.block_tables = block_table.to(device)
with set_ascend_forward_context(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],
)
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)
# Generate the remaining draft tokens.
draft_token_ids_tensor = torch.zeros(
(self.num_speculative_tokens, *draft_token_ids.shape),
dtype=draft_token_ids.dtype)
draft_token_ids_tensor[0] = draft_token_ids
positions_cpu = target_positions[last_token_indices].cpu().to(
torch.int64)
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 self.num_speculative_tokens > 2:
raise ValueError("Speculative tokens > 2 are not supported yet.")
attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
for now_speculative 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_tensor[now_speculative].to(device)
positions_cpu += 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_cpu >= 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_cpu = torch.where(exceeds_max_model_len, 0,
positions_cpu)
clamped_positions = clamped_positions_cpu.to(device)
# TODO: Increment the sequence lengths.
attn_metadata.seq_lens += 1
# TODO: 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
# TODO: sequence length to 1 to minimize their overheads in attention.
# Compute the slot mapping.
block_numbers = (clamped_positions_cpu // self.block_size)
block_ids = block_table.gather(dim=1,
index=block_numbers.view(-1, 1))
block_ids = block_ids.view(-1)
slot_mapping_cpu = (block_ids * self.block_size +
clamped_positions_cpu % 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_cpu.masked_fill_(exceeds_max_model_len,
PADDING_SLOT_ID)
# NOTE: ASCEND slot_mapping must on cpu
attn_metadata.slot_mapping = slot_mapping_cpu.to(
torch.int32).to(device)
# 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
positions = positions_cpu.to(device)
attn_mask = self._make_attention_mask(
seq_lens=attn_metadata.seq_lens,
position=positions,
)
attn_metadata.attn_mask = attn_mask
attn_metadata.block_tables = block_table.to(device)
# Run the model.
with set_ascend_forward_context(attn_metadata,
self.vllm_config,
num_tokens=input_batch_size):
last_hidden_states, hidden_states = self.model(
input_ids=self.input_ids[:input_batch_size],
positions=self.positions[:input_batch_size],
hidden_states=self.hidden_states[:input_batch_size],
)
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_tensor[now_speculative + 1] = draft_token_ids.cpu()
# [batch_size, num_speculative_tokens]
draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
return draft_token_ids
@staticmethod
def prepare_inputs(
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
num_rejected_tokens: torch.Tensor,
num_tokens: int,
) -> tuple[torch.Tensor, torch.Tensor]:
# cu_target_query_lens: [0, a, a + b, a + b + c]
# num_rejected_tokens: [n1, n2, n3]
# num_tokens_per_req: [a - n1, b - n2, c - n3]
# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
# token_indices: [0, 1, ..., a - n1 - 1,
# a, a + 1, ..., a + b - n2 - 1,
# a + b, a + b + 1, ..., a + b + c - n3 - 1]
# [0, a, a + b, a + b + c] -> [a, b, c]
query_len_per_req = (cu_target_query_lens[1:] -
cu_target_query_lens[:-1])
# [a, b, c] -> [a - n1, b - n2, c - n3]
num_tokens_per_req = query_len_per_req - num_rejected_tokens
# [a - n1, b - n2, c - n3] ->
# [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
cu_num_tokens = torch.zeros_like(cu_target_query_lens)
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
token_indices = torch.empty(
num_tokens,
dtype=torch.int32,
device=cu_target_query_lens.device,
)
BLOCK_SIZE = 1024
prepare_eagle_input_sequential(
token_indices,
cu_target_query_lens,
cu_num_tokens,
block_size=BLOCK_SIZE,
)
return cu_num_tokens, token_indices
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())
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)
self.attn_layer_names = list(draft_attn_layer_names)
self.attn_layer_name = next(iter(draft_attn_layer_names))
# share embed_tokens with the target model if needed
if get_pp_group().world_size == 1:
logger.info(
"The EAGLE head shares the same vocab embedding" \
" with the target model."
)
self.model.model.embed_tokens = target_model.model.embed_tokens
else:
logger.info(
"Since PP > 1, the EAGLE head loaded its own vocab embedding" \
" weights instead of sharing them with 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" and \
hasattr(target_model, "lm_head"):
logger.info("Loading EAGLE LM head weights from the target model.")
if supports_multimodal(target_model):
self.model.lm_head = target_model.get_language_model().lm_head
else:
self.model.lm_head = target_model.lm_head
@torch.inference_mode()
def dummy_run(
self,
num_tokens: int,
) -> None:
with set_ascend_forward_context(None,
self.vllm_config,
num_tokens=num_tokens):
self.model(
input_ids=self.input_ids[:num_tokens],
positions=self.positions[:num_tokens],
hidden_states=self.hidden_states[:num_tokens],
)
def prepare_eagle_input_sequential(out_tensor: torch.Tensor,
cu_query_lens: torch.Tensor,
cu_num_tokens: torch.Tensor,
block_size: int):
num_programs = len(cu_num_tokens) - 1
for pid in range(num_programs):
start_pos = cu_num_tokens[pid].item()
end_pos = cu_num_tokens[pid + 1].item()
num_tokens = end_pos - start_pos
index_start = cu_query_lens[pid].item()
num_blocks = int(
torch.ceil(torch.tensor(num_tokens / block_size)).item())
for i in range(num_blocks):
offset_tensor = torch.arange(0,
block_size,
dtype=torch.int32,
device=out_tensor.device)
global_start_offset = i * block_size
target_indices = torch.tensor(
start_pos + global_start_offset,
dtype=torch.int32,
device=out_tensor.device) + offset_tensor
values_to_store = torch.tensor(
index_start, dtype=torch.int32,
device=out_tensor.device) + offset_tensor
mask = (target_indices >= start_pos) & \
(target_indices < end_pos) & \
(offset_tensor < num_tokens)
out_tensor[target_indices[mask]] = values_to_store[mask]

421
vllm_ascend/worker/model_runner_v1.py
View File

@@ -86,14 +86,16 @@ from vllm_ascend.compilation.acl_graph import ACLGraphWrapper
from vllm_ascend.multistream.ms_split import compute_split_seq_index
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.sample.rejection_sampler import AscendRejectionSampler
from vllm_ascend.spec_decode import get_spec_decode_method
from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
from vllm_ascend.spec_decode.interface import SpecDcodeType
from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
from vllm_ascend.torchair.torchair_attention import AscendTorchairMetadata
from vllm_ascend.torchair.torchair_mla import AscendMLATorchairMetadata
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
AscendSocVersion, ProfileExecuteDuration,
get_ascend_soc_version, is_310p,
lmhead_tp_enable, vllm_version_is)
from vllm_ascend.worker.eagle_proposer_v1 import EagleProposer
from vllm_ascend.worker.mtp_proposer_v1 import MtpProposer
from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
if not (vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1")):
@@ -235,16 +237,12 @@ class NPUModelRunner(LoRAModelRunnerMixin):
self.model_config.max_model_len, self.dtype)
# Set up speculative decoding.
self.use_aux_hidden_state_outputs = False
self.use_spec_decode = False
self.spec_attn_mask = None
self.use_eagle = False
self.drafter: Optional[Union[NgramProposer, EagleProposer,
MtpProposer]] = None
self.actual_seq_lengths_q = []
self.decode_token_per_req = 1
if self.speculative_config:
self.use_spec_decode = True
spec_token_num = self.speculative_config.num_speculative_tokens
assert spec_token_num > 0
self.decode_token_per_req = 1 + spec_token_num
@@ -258,19 +256,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
dtype=torch.bool),
diagonal=1).to(self.device)
if get_pp_group().is_last_rank:
if self.speculative_config.method == "ngram":
self.drafter = NgramProposer(self.vllm_config)
elif self.speculative_config.method in ["eagle", "eagle3"]:
self.use_eagle = True
self.drafter = EagleProposer(self.vllm_config, self.device,
self) # type: ignore
if self.speculative_config.method == "eagle3":
self.use_aux_hidden_state_outputs = True
elif self.speculative_config.method == 'deepseek_mtp':
self.drafter = MtpProposer(self.vllm_config, self)
else:
raise ValueError("Unknown speculative decoding method: "
f"{self.speculative_config.method}")
self.drafter = get_spec_decode_method(
self.speculative_config.method, self.vllm_config,
self.device, self)
self.rejection_sampler = AscendRejectionSampler()
# Persistent batch.
@@ -649,152 +637,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
return False
return True
def get_eagle_atten_dict(
self,
scheduler_output: "SchedulerOutput",
) -> dict[str, Union[AscendMetadata, AscendMLAMetadata,
AscendTorchairMetadata, AscendMLATorchairMetadata]]:
total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
assert total_num_scheduled_tokens > 0
num_reqs = self.input_batch.num_reqs
assert num_reqs > 0
# OPTIMIZATION: Start copying the block table first.
# This way, we can overlap the copy with the following CPU operations.
self.input_batch.block_table.commit_block_table(num_reqs)
# Get the number of scheduled tokens for each request.
req_ids = self.input_batch.req_ids
tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
num_scheduled_tokens = np.array(tokens, dtype=np.int32)
max_num_scheduled_tokens = max(tokens)
self.query_lens = torch.from_numpy(num_scheduled_tokens)
# Get request indices.
# E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
req_indices = np.repeat(self.arange_np[:num_reqs],
num_scheduled_tokens)
# cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
# arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
cu_num_tokens, arange = self._get_cumsum_and_arange(
num_scheduled_tokens)
# Get positions.
positions_np = self.positions_np[:total_num_scheduled_tokens]
np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
arange,
out=positions_np)
# Calculate M-RoPE positions.
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.uses_mrope:
self._calc_mrope_positions(scheduler_output)
# Get token indices.
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
# where M is the max_model_len.
token_indices = (positions_np +
req_indices * self.input_batch.token_ids_cpu.shape[1])
# NOTE(woosuk): We use torch.index_select instead of np.take here
# because torch.index_select is much faster than np.take for large
# tensors.
torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
0,
torch.from_numpy(token_indices),
out=self.input_ids_cpu[:total_num_scheduled_tokens])
# Prepare the attention metadata for each KV cache group and make layers
# in the same group share the same metadata.
# NOTE(Chen): there is exactly one KV cache group that contains all
# attetnion layers in the model for now, so the current logic for
# getting attn_metadata is not related to kv_cache_group information.
# Will extend this part to support multiple KV cache groups later.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.kv_cache_config.kv_cache_groups):
block_size = kv_cache_group_spec.kv_cache_spec.block_size
block_table = self.input_batch.block_table[kv_cache_group_id]
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
# where K is the max_num_blocks_per_req and the block size is 2.
# NOTE(woosuk): We can't simply use `token_indices // block_size`
# here because M (max_model_len) is not necessarily divisible by
# block_size.
block_table_indices = (
req_indices * block_table.max_num_blocks_per_req +
positions_np // block_size)
block_table_cpu = block_table.get_cpu_tensor()
block_numbers = block_table_cpu.flatten(
)[block_table_indices].numpy()
block_offsets = positions_np % block_size
np.add(
block_numbers * block_size,
block_offsets,
out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
# Prepare the attention metadata.
self.query_start_loc_np[0] = 0
self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
self.seq_lens_np[:num_reqs] = (
self.input_batch.num_computed_tokens_cpu[:num_reqs] +
num_scheduled_tokens)
# Copy the tensors to the NPU.
self.input_ids[:total_num_scheduled_tokens].copy_(
self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
if self.uses_mrope:
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
non_blocking=True)
else:
# Common case (1D positions)
self.positions[:total_num_scheduled_tokens].copy_(
self.positions_cpu[:total_num_scheduled_tokens],
non_blocking=True)
self.query_start_loc[:num_reqs + 1].copy_(
self.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
non_blocking=True)
# Fill unused with -1. Needed for reshape_and_cache
self.seq_lens[num_reqs:].fill_(0)
self.query_start_loc[num_reqs + 1:].fill_(-1)
attn_metadata: dict[str, Union[AscendMetadata, AscendMLAMetadata,
AscendTorchairMetadata,
AscendMLATorchairMetadata]] = {}
# Prepare the attention metadata for each KV cache group and make layers
# in the same group share the same metadata.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.kv_cache_config.kv_cache_groups):
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=self.query_start_loc[:num_reqs + 1],
query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs + 1],
seq_lens_cpu=self.seq_lens_cpu,
num_reqs=num_reqs,
max_query_len=max_num_scheduled_tokens,
num_actual_tokens=total_num_scheduled_tokens,
actual_seq_lengths_q=self.actual_seq_lengths_q,
block_table_tensor=self.input_batch.block_table[0].
get_device_tensor(),
slot_mapping_cpu=self.slot_mapping_cpu,
positions=self.positions,
attn_mask=self.attn_mask,
spec_attn_mask=self.spec_attn_mask,
attn_state=self.attn_state,
decode_token_per_req=self.decode_token_per_req,
)
attn_metadata_i = self.attn_metadata_builder.build(
common_attn_metadata, self.get_model())
for layer_name in kv_cache_group_spec.layer_names:
attn_metadata[layer_name] = attn_metadata_i
return attn_metadata
def get_model(self) -> nn.Module:
# get raw model out of the aclgraph wrapper.
if isinstance(self.model, ACLGraphWrapper):
@@ -1317,7 +1159,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
attn_state = AscendAttentionState.SpecDecoding
# Speculative decoding.
elif np.all(num_valid_tokens == 1):
if self.use_eagle:
if self.drafter and (self.drafter.name == SpecDcodeType.EAGLE
or self.drafter.name == SpecDcodeType.EAGLE3):
attn_state = AscendAttentionState.ChunkedPrefill
else:
attn_state = AscendAttentionState.SpecDecoding
@@ -1338,26 +1181,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
positions = self.mrope_positions[:, :num_input_tokens]
return input_ids, positions
def _get_cumsum_and_arange(
self,
num_tokens: np.ndarray,
cumsum_dtype: Optional[np.dtype] = None,
) -> tuple[np.ndarray, np.ndarray]:
"""Get the cumulative sum and batched arange of the given array.
# E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
# Equivalent to but faster than:
# np.concatenate([np.arange(n) for n in num_tokens])
"""
# Step 1. [2, 5, 3] -> [2, 7, 10]
cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
total_num_tokens = cu_num_tokens[-1]
# Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
# Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
arange = self.arange_np[:total_num_tokens] - cumsums_offsets
return cu_num_tokens, arange
def _calc_spec_decode_metadata(
self,
num_draft_tokens: np.ndarray,
@@ -1511,24 +1334,14 @@ class NPUModelRunner(LoRAModelRunnerMixin):
AscendMLATorchairMetadata],
aux_hidden_states: torch.Tensor = None,
) -> Optional[list[list[int]]]:
if not self.use_spec_decode:
if not self.drafter:
# Speculative decoding is not enabled.
draft_token_ids = None
elif self.speculative_config.method == "ngram":
draft_token_ids = self._generate_ngram_token_ids(
valid_sampled_token_ids)
elif self.speculative_config.method == "eagle":
raise NotImplementedError("Eagle Is Not Supported Yet.")
elif self.speculative_config.method == "eagle3":
draft_token_ids = self._generate_eagle3_token_ids(
else:
draft_token_ids = self.drafter.generate_token_ids(
valid_sampled_token_ids, sampling_metadata, scheduler_output,
spec_decode_metadata, positions, num_scheduled_tokens,
hidden_states, aux_hidden_states)
elif self.speculative_config.method == 'deepseek_mtp':
draft_token_ids = self._generate_mtp_token_ids(
valid_sampled_token_ids, sampling_metadata, scheduler_output,
spec_decode_metadata, positions, num_scheduled_tokens,
hidden_states, attn_metadata)
hidden_states, attn_metadata, aux_hidden_states)
return draft_token_ids
def _pool_v010(
@@ -1692,7 +1505,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
scheduler_output)
aux_hidden_states = None
if self.use_aux_hidden_state_outputs:
if self.drafter and self.drafter.name == SpecDcodeType.EAGLE3:
hidden_states, aux_hidden_states = hidden_states
kv_connector_output = None
@@ -1981,12 +1794,10 @@ class NPUModelRunner(LoRAModelRunnerMixin):
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds)
if self.use_aux_hidden_state_outputs:
if self.drafter and self.drafter.name == SpecDcodeType.EAGLE3:
hidden_states, _ = hidden_states
else:
hidden_states = hidden_states
if self.use_spec_decode and isinstance(self.drafter, EagleProposer):
self.drafter.dummy_run(num_tokens)
return hidden_states
@torch.inference_mode()
@@ -2137,8 +1948,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
if need_dummy_logits:
dummy_compute_logits(hidden_states)
if self.speculative_config and self.speculative_config.method == "deepseek_mtp":
assert isinstance(self.drafter, MtpProposer)
if self.drafter:
self.drafter.dummy_run(
num_tokens=num_tokens,
with_prefill=with_prefill,
@@ -2296,13 +2106,11 @@ class NPUModelRunner(LoRAModelRunnerMixin):
module.weight.data)
if self.drafter:
logger.info("Loading drafter model...")
if isinstance(self.drafter, EagleProposer):
if self.use_aux_hidden_state_outputs:
self.drafter.load_model(self.model)
self.model.set_aux_hidden_state_layers(
self.model.get_eagle3_aux_hidden_state_layers())
else:
self.drafter.load_model()
self.drafter.load_model(self.model)
if self.drafter.name == SpecDcodeType.EAGLE3:
self.model.set_aux_hidden_state_layers(
self.model.get_eagle3_aux_hidden_state_layers())
if self.lora_config:
self.model = self.load_lora_model(self.model,
self.model_config,
@@ -2590,193 +2398,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
elapsed_time, npu_graph_size / (1 << 30))
def _generate_ngram_token_ids(
self,
sampled_token_ids: list[list[int]],
) -> list[list[int]]:
# TODO(woosuk): Optimize.
draft_token_ids: list[list[int]] = []
for i, sampled_ids in enumerate(sampled_token_ids):
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
draft_token_ids.append([])
continue
# Skip requests that require top-p, top-k, etc.
req_id = self.input_batch.req_ids[i]
if req_id in self.input_batch.spec_decode_unsupported_reqs:
draft_token_ids.append([])
continue
# Add sampled_token_ids to token_ids_cpu.
start_idx = self.input_batch.num_tokens_no_spec[i]
end_idx = start_idx + num_sampled_ids
self.input_batch.token_ids_cpu[i, start_idx:end_idx] = sampled_ids
assert isinstance(self.drafter, NgramProposer)
drafter_output = self.drafter.propose(
self.input_batch.token_ids_cpu[i, :end_idx])
if drafter_output is None or len(drafter_output) == 0:
draft_token_ids.append([])
else:
draft_token_ids.append(drafter_output.tolist())
return draft_token_ids
def _generate_eagle3_token_ids(self,
valid_sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata,
scheduler_output: "SchedulerOutput",
spec_decode_metadata: SpecDecodeMetadata,
positions: torch.Tensor,
num_scheduled_tokens: int,
hidden_states: torch.Tensor,
aux_hidden_states: torch.Tensor = None):
assert isinstance(self.drafter, EagleProposer)
attn_metadata = self.get_eagle_atten_dict(scheduler_output)
next_token_ids: list[int] = []
for i, token_ids in enumerate(valid_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 = self.input_batch.req_ids[i]
req_state = self.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.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.device)
eagle_attn_metadata = attn_metadata[self.drafter.attn_layer_name]
if spec_decode_metadata is None:
# input_ids can be None for multimodal models.
target_token_ids = self.input_ids[:num_scheduled_tokens]
target_positions = positions[:num_scheduled_tokens]
if self.use_aux_hidden_state_outputs:
target_hidden_states = torch.cat(
[h[:num_scheduled_tokens] for h in aux_hidden_states],
dim=-1)
else:
target_hidden_states = hidden_states[:num_scheduled_tokens]
target_slot_mapping = eagle_attn_metadata.slot_mapping
cu_num_tokens = eagle_attn_metadata.query_start_loc
else:
num_draft_tokens = spec_decode_metadata.num_draft_tokens
num_rejected_tokens = [
n + 1 - len(valid_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=self.device,
)
num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
cu_num_tokens, token_indices = self.drafter.prepare_inputs(
eagle_attn_metadata.query_start_loc, num_rejected_tokens,
num_tokens)
target_token_ids = self.input_ids[token_indices]
target_positions = positions[token_indices]
if self.use_aux_hidden_state_outputs:
target_hidden_states = torch.cat(
[h[token_indices] for h in aux_hidden_states], dim=-1)
else:
target_hidden_states = hidden_states[token_indices]
target_slot_mapping = eagle_attn_metadata.slot_mapping[
token_indices]
draft_token_ids = self.drafter.propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
target_slot_mapping=target_slot_mapping,
next_token_ids=next_token_ids,
cu_num_tokens=cu_num_tokens,
block_table=eagle_attn_metadata.block_tables,
sampling_metadata=sampling_metadata,
)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids
def _generate_mtp_token_ids(
self,
valid_sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata,
scheduler_output: "SchedulerOutput",
spec_decode_metadata: SpecDecodeMetadata,
positions: torch.Tensor,
num_scheduled_tokens: int,
hidden_states: torch.Tensor,
attn_metadata: Union[AscendMetadata, AscendMLAMetadata,
AscendTorchairMetadata,
AscendMLATorchairMetadata],
):
assert isinstance(self.drafter, MtpProposer)
next_token_ids: list[int] = []
for i, token_ids in enumerate(valid_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 = self.input_batch.req_ids[i]
req_state = self.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.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.device)
accepted_token_indices = None
if spec_decode_metadata is None:
# input_ids can be None for multimodal models.
target_token_ids = self.input_ids[:num_scheduled_tokens]
target_positions = positions[:num_scheduled_tokens]
target_hidden_states = hidden_states[:num_scheduled_tokens]
target_slot_mapping = attn_metadata.slot_mapping
cu_num_tokens = attn_metadata.query_start_loc
else:
# TODO(woosuk): Refactor this.
num_draft_tokens = spec_decode_metadata.num_draft_tokens
num_rejected_tokens = [
n + 1 - len(valid_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=self.device,
)
cu_num_tokens, accepted_token_indices, target_token_ids, \
target_positions, target_hidden_states, target_slot_mapping = self.drafter.prepare_inputs(
attn_metadata.query_start_loc,
num_rejected_tokens,
self.input_ids[:num_scheduled_tokens],
positions[:num_scheduled_tokens],
hidden_states[:num_scheduled_tokens],
attn_metadata.slot_mapping[:num_scheduled_tokens],
is_torchair_graph=self._build_drafter_prepare_inputs_torchair_param(),
)
draft_token_ids = self.drafter.propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
target_slot_mapping=target_slot_mapping,
next_token_ids=next_token_ids,
cu_num_tokens=cu_num_tokens,
block_table=attn_metadata.block_tables,
sampling_metadata=sampling_metadata,
token_indices=accepted_token_indices)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids
def _get_prompt_logprobs_dict(
self,
hidden_states: torch.Tensor,