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[FEAT] Refactor spec decode to support efficient padded speculation (#3528)

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### What this PR does / why we need it?
1. Refactor the file `mtp_proposer.py`, splits torchair related codes
into `mtp_torchair_proposer.py`
2. According to https://github.com/vllm-project/vllm/pull/24539,
implements padded speculative decoding as described in
https://github.com/vllm-project/vllm/issues/21984.
### Does this PR introduce _any_ user-facing change?
User can use `disable_padded_drafter_batch` to disable/enable padded
speculation, default is `False`.
offline example:
```
speculative_config={"method": "deepseek_mtp", "num_speculative_tokens": 1, "disable_padded_drafter_batch": False}
```

### How was this patch tested?

- [x] egaer with pad/unpad:
- [x] aclgraph with pad/unpad
- [x] torchair with pad/unpad

performance test of deepseek-r1 with tp16、dp1
aclgraph with pad ITL: 168ms
aclgraph with unpad ITL: 169ms
original: 178ms


- vLLM version: v0.11.0rc3
- vLLM main:
83f478bb19

---------

Signed-off-by: xuyexiong <xuyexiong@huawei.com>
This commit is contained in:
xuyexiong
2025-10-30 16:53:05 +08:00
committed by GitHub
parent 10772d94e3
commit eff3e5fc6f
7 changed files with 1203 additions and 440 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
vllm_ascend/spec_decode/__init__.py
View File

@@ -19,14 +19,21 @@
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
from vllm_ascend.torchair.torchair_mtp_proposer import TorchairMtpProposer
def get_spec_decode_method(method, vllm_config, device, runner):
def get_spec_decode_method(method,
vllm_config,
device,
runner,
is_torchair_graph=False):
if method == "ngram":
return NgramProposer(vllm_config, device, runner)
elif method in ["eagle", "eagle3"]:
return EagleProposer(vllm_config, device, runner)
elif method == 'deepseek_mtp':
if is_torchair_graph:
return TorchairMtpProposer(vllm_config, device, runner)
return MtpProposer(vllm_config, device, runner)
else:
raise ValueError("Unknown speculative decoding method: "

831
vllm_ascend/spec_decode/mtp_proposer.py
View File

@@ -1,37 +1,41 @@
import types
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
import torchair
from torchair import patch_for_hcom
from vllm.config import (CUDAGraphMode, VllmConfig,
get_layers_from_vllm_config, set_current_vllm_config)
from vllm.forward_context import BatchDescriptor, get_forward_context
from vllm.forward_context import BatchDescriptor
from vllm.logger import init_logger
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.model_loader import get_model_loader
from vllm.model_executor.model_loader.utils import \
process_weights_after_loading
from vllm.model_executor.models.deepseek_mtp import DeepSeekMTP
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
CommonAttentionMetadata)
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.v1.utils import CpuGpuBuffer
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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.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,
TorchairCommonAttentionMetadata)
from vllm_ascend.utils import (ProfileExecuteDuration, lmhead_tp_enable,
vllm_version_is)
if vllm_version_is("0.11.0"):
from vllm.model_executor.model_loader.utils import set_default_torch_dtype
from vllm.utils import is_pin_memory_available
else:
from vllm.utils.platform_utils import is_pin_memory_available
from vllm.utils.torch_utils import set_default_torch_dtype
logger = init_logger(__name__)
PADDING_SLOT_ID = -1
@@ -45,34 +49,77 @@ class MtpProposer(Proposer):
):
self.name = SpecDcodeType.MTP
self.vllm_config = vllm_config
self.device = device
self.runner = runner
self.num_speculative_tokens = vllm_config.speculative_config.num_speculative_tokens
self.speculative_config = vllm_config.speculative_config
assert self.speculative_config is not None
self.draft_model_config = self.speculative_config.draft_model_config
self.method = self.speculative_config.method
# persistent buffers for graph
self.input_ids = torch.zeros(self.runner.max_num_tokens,
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()
self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None
self.draft_indexer_metadata_builder: Optional[
AttentionMetadataBuilder] = None
self.attn_layer_names: list[str] = []
self.indexer_layer_names: list[str] = []
self.use_aclgraph = self.runner._use_aclgraph()
self.cudagraph_batch_sizes = (list(
reversed(
self.vllm_config.compilation_config.cudagraph_capture_sizes))
if self.use_aclgraph else [])
# persistent buffers for aclgraph graph
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device=self.device)
self.positions = torch.zeros(self.runner.max_num_tokens,
dtype=torch.int64,
device=self.device)
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.runner.max_num_tokens,
vllm_config.model_config.get_hidden_size()),
dtype=self.runner.dtype,
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
self.enable_shared_expert_dp = get_ascend_config(
).enable_shared_expert_dp
(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=self.runner.device,
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,
)
self.use_sparse = hasattr(vllm_config.model_config.hf_config,
"index_topk")
@@ -89,14 +136,8 @@ class MtpProposer(Proposer):
with set_default_torch_dtype(
draft_model_config.dtype), set_current_vllm_config(
self.vllm_config):
if self.torchair_graph_enabled or (
self.enable_shared_expert_dp
and self.vllm_config.model_config.use_mla):
self.model = TorchairDeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
else:
self.model = DeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
self.model = DeepSeekMTP(
vllm_config=self.vllm_config).to(target_device)
draft_attn_layer_names = (get_layers_from_vllm_config(
self.vllm_config, AttentionLayerBase).keys() -
@@ -121,34 +162,17 @@ class MtpProposer(Proposer):
num_tokens_across_dp=None,
aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
batch_descriptor=None) -> None:
if not self.torchair_graph_enabled:
(
num_tokens,
num_tokens_across_dp,
with_prefill,
) = self.runner._sync_metadata_across_dp(num_tokens, with_prefill)
(
num_tokens,
num_tokens_across_dp,
with_prefill,
) = self.runner._sync_metadata_across_dp(num_tokens, with_prefill)
moe_comm_type = self.runner._select_moe_comm_method(
num_tokens, with_prefill)
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)
attn_metadata = None
input_ids = self.input_ids[:num_tokens]
positions = self.positions[:num_tokens]
@@ -166,40 +190,14 @@ class MtpProposer(Proposer):
num_actual_tokens=0,
aclgraph_runtime_mode=aclgraph_runtime_mode,
batch_descriptor=batch_descriptor):
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,
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,
hidden_states=previous_hidden_states)
self.model(input_ids=input_ids,
positions=positions,
hidden_states=previous_hidden_states)
if with_prefill:
break
def generate_token_ids(self,
valid_sampled_token_ids: list[list[int]],
sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata = None,
scheduler_output: SchedulerOutput = None,
spec_decode_metadata: SpecDecodeMetadata = None,
@@ -208,235 +206,240 @@ class MtpProposer(Proposer):
hidden_states: torch.Tensor = None,
attn_metadata=None,
aux_hidden_states: torch.Tensor = None):
common_attn_metadata = self.runner.spec_decode_common_attn_metadata
if attn_metadata is not None and isinstance(attn_metadata, dict):
attn_metadata = attn_metadata['model.layers.0.self_attn.attn']
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 self.speculative_config.disable_padded_drafter_batch:
# When padded-batch is disabled, the sampled_token_ids should be
# the cpu-side list[list[int]] of valid sampled tokens for each
# request, with invalid requests having empty lists.
assert isinstance(sampled_token_ids, list), \
"sampled_token_ids should be a python list when" \
"padded-batch is disabled."
next_token_ids = self.prepare_next_token_ids_cpu(
sampled_token_ids, self.runner.requests,
self.runner.input_batch, scheduler_output.num_scheduled_tokens)
else:
# When using padded-batch, the sampled_token_ids should be
# the gpu tensor of sampled tokens for each request, of shape
# (num_reqs, num_spec_tokens + 1) with rejected tokens having
# value -1.
assert isinstance(sampled_token_ids, torch.Tensor), \
"sampled_token_ids should be a torch.Tensor when" \
"padded-batch is enabled."
next_token_ids, valid_sampled_tokens_count = \
self.prepare_next_token_ids_padded(
common_attn_metadata,
sampled_token_ids,
self.runner.requests,
self.runner.input_batch,
self.runner.discard_request_indices.gpu,
self.runner.num_discarded_requests
)
if spec_decode_metadata is None:
token_indices_to_sample = 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(),
)
if self.speculative_config.disable_padded_drafter_batch:
token_indices_to_sample = None
common_attn_metadata, token_indices =\
self._prepare_inputs(
common_attn_metadata,
sampled_token_ids,
spec_decode_metadata.num_draft_tokens)
else:
common_attn_metadata, token_indices, \
token_indices_to_sample =\
self.prepare_inputs_padded(
common_attn_metadata,
spec_decode_metadata,
valid_sampled_tokens_count)
target_token_ids = self.runner.input_ids[token_indices]
target_positions = positions[token_indices]
target_hidden_states = hidden_states[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=attn_metadata.block_tables,
last_token_indices=token_indices_to_sample,
common_attn_metadata=common_attn_metadata,
sampling_metadata=sampling_metadata,
token_indices=accepted_token_indices)
spec_token_ids = draft_token_ids.tolist()
return spec_token_ids
)
return draft_token_ids
def _prepare_inputs(
self,
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
num_rejected_tokens: torch.Tensor,
token_ids: torch.Tensor,
positions: torch.Tensor,
hidden_states: torch.Tensor,
slot_mapping: torch.Tensor,
is_torchair_graph: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
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
if is_torchair_graph:
cu_num_tokens = cu_target_query_lens
relative_index = query_len_per_req - num_rejected_tokens - 1
token_indices = cu_num_tokens[:-1] + relative_index
# the seq len of each bath is padded to 1+num_speculative_tokens, thus input is same as the main model
target_token_ids = token_ids
target_positions = positions
target_hidden_states = hidden_states
target_slot_mapping = slot_mapping
else:
cu_num_tokens = torch.empty_like(cu_target_query_lens)
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
cu_num_tokens[0] = 0
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]
# FIXME(woosuk): Avoid synchronization.
num_tokens = cu_num_tokens[-1].item()
token_indices = torch.zeros(
num_tokens,
dtype=torch.int32,
device=cu_num_tokens.device,
)
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)
BLOCK_SIZE = 1024
self._prepare_input_kernel(
token_indices,
cu_target_query_lens,
cu_num_tokens,
block_size=BLOCK_SIZE,
)
target_token_ids = token_ids[token_indices]
target_positions = positions[token_indices]
target_hidden_states = hidden_states[token_indices]
target_slot_mapping = slot_mapping[token_indices]
return cu_num_tokens, token_indices, target_token_ids, target_positions, target_hidden_states, target_slot_mapping
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 = AscendCommonAttentionMetadata(
query_start_loc=new_query_start_loc_cpu.to(device,
non_blocking=True),
query_start_loc_cpu=new_query_start_loc_cpu,
seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
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(),
block_table_tensor=common_attn_metadata.block_table_tensor,
slot_mapping=common_attn_metadata.slot_mapping[token_indices],
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
positions=common_attn_metadata.positions[token_indices],
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
graph_pad_size=self.runner.graph_pad_size,
decode_token_per_req=self.runner.decode_token_per_req,
)
return spec_common_attn_metadata, token_indices
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,
token_indices=None) -> torch.Tensor:
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]
last_token_indices = cu_num_tokens[1:] - 1
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]
if token_indices is not None and self.torchair_graph_enabled:
last_token_indices = token_indices
self.input_ids[last_token_indices] = next_token_ids
query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
max_query_len = query_lens.max().item()
assert self.runner is not None
# FIXME: reorder_batch() needs to be called before build()
# because fields of attn_metadata_builder needs to be updated.
# However, currently reorder_batch() takes input_batch and
# scheduler_output as arguments, we should probably refactor
# the method to use new data structures which are independent
# from input_batch and scheduler_output.
# self.runner.attn_metadata_builder.reorder_batch(
# input_batch=self.runner.input_batch,
# scheduler_output=self.runner.scheduler_output,
# )
is_running_torchair = self.torchair_graph_enabled and \
not self.runner.with_prefill
builder = self.runner.attn_groups[0][0].get_metadata_builder()
attn_metadata_mtp = builder.build(0, common_attn_metadata,
self.runner.get_model())
attn_metadata = {}
for layer_name in self.attn_layer_name:
attn_metadata[layer_name] = attn_metadata_mtp
if is_running_torchair:
# Torchair graph mode, padding is same as the main model
num_input_tokens = self.runner.graph_pad_size
elif (self.runner.use_aclgraph
and num_tokens <= self.runner.aclgraph_batch_sizes[-1]):
if self.use_aclgraph and num_tokens <= self.cudagraph_batch_sizes[-1]:
# Acl graph mode, add padding to the batch size
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
else:
# Eager mode, no padding needed
num_input_tokens = num_tokens
seq_lens = target_positions[last_token_indices] + 1
seq_lens = seq_lens.int()
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=cu_num_tokens[:batch_size + 1],
query_start_loc_cpu=cu_num_tokens[:batch_size + 1].cpu(),
seq_lens_cpu=seq_lens.cpu(),
num_reqs=batch_size,
num_actual_tokens=num_tokens,
max_query_len=max_query_len,
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=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,
graph_pad_size=self.runner.graph_pad_size,
decode_token_per_req=self.runner.decode_token_per_req,
num_computed_tokens_cpu=None,
seq_lens=None)
if not self.torchair_graph_enabled:
builder = self.runner.attn_groups[0][0].get_metadata_builder()
attn_metadata_mtp = builder.build(0, common_attn_metadata,
self.runner.get_model())
attn_metadata = {}
for layer_name in self.attn_layer_name:
attn_metadata[layer_name] = attn_metadata_mtp
else:
attn_metadata = self.runner.attn_metadata_builder.build(
0, common_attn_metadata, self.runner.get_model())
# copy inputs to buffer for cudagraph
self.positions[:num_tokens] = target_positions
self.hidden_states[:num_tokens] = target_hidden_states
if not self.torchair_graph_enabled:
# torch mode need to update num_tokens_across_dp
(num_input_tokens, num_tokens_across_dp,
with_prefill) = self.runner._sync_metadata_across_dp(
num_input_tokens, self.runner.with_prefill)
else:
# torchair mode can reuse self.runner.num_tokens_across_dp
num_tokens_across_dp = self.runner.num_tokens_across_dp
with_prefill = self.runner.with_prefill
# eager/acl piecewise mode need to update num_tokens_across_dp
(num_input_tokens, num_tokens_across_dp,
with_prefill) = self.runner._sync_metadata_across_dp(
num_input_tokens, self.runner.with_prefill)
moe_comm_type = self.runner._select_moe_comm_method(
num_input_tokens, with_prefill)
@@ -444,6 +447,15 @@ class MtpProposer(Proposer):
uniform_decode=False)
aclgraph_runtime_mode, batch_descriptor = \
self.runner.aclgraph_dispatcher.dispatch(batch_descriptor)
if aclgraph_runtime_mode not in [
CUDAGraphMode.PIECEWISE, CUDAGraphMode.NONE
]:
# Fallback to piecewise graph, when acl full graph is enabled
logger.debug(
"Currently the eagle proposer only supports cudagraph_mode "
f"PIECEWISE, and is forced to set graph mode from {aclgraph_runtime_mode} "
"to CUDAGraphMode.PIECEWISE")
aclgraph_runtime_mode = CUDAGraphMode.PIECEWISE
for step in range(self.num_speculative_tokens):
with set_ascend_forward_context(
@@ -461,26 +473,11 @@ class MtpProposer(Proposer):
with ProfileExecuteDuration().capture_async('mtp_forward'):
model_kwargs = {}
model_kwargs["attn_metadata"] = attn_metadata
if self.torchair_graph_enabled:
model_kwargs["kv_caches"] = self.runner.kv_caches[-1:]
if is_running_torchair:
torchair_compiled_model = self._get_torchair_lazy_compiled_model(
num_input_tokens)
hidden_states = torchair_compiled_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,
intermediate_tensors=None,
spec_step_idx=0,
**model_kwargs)
else:
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]
)
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])
num_indices = last_token_indices.shape[0]
if lmhead_tp_enable():
@@ -515,10 +512,7 @@ class MtpProposer(Proposer):
if step == self.num_speculative_tokens - 1 or with_prefill:
break
if not self.torchair_graph_enabled:
attn_metadata_i = attn_metadata[self.attn_layer_name[0]]
else:
attn_metadata_i = attn_metadata
attn_metadata_i = attn_metadata[self.attn_layer_name[0]]
if step == 0:
positions = target_positions[last_token_indices]
@@ -529,21 +523,16 @@ class MtpProposer(Proposer):
last_token_indices = self.arange[:batch_size]
if attn_metadata_i.num_decode_tokens != 0:
attn_metadata_i.num_decode_tokens = batch_size
if is_running_torchair:
attn_metadata_i.num_actual_tokens = batch_size
attn_metadata_i.query_lens = [1] * batch_size
input_ids = draft_token_ids_list[-1].int()
positions += 1
if not self.torchair_graph_enabled:
attn_metadata_i.decode.actual_seq_lengths_q = attn_metadata_i.query_start_loc[
1:batch_size + 1].tolist()
attn_metadata_i.decode.cos = builder.cos_cache[
positions].unsqueeze(1).unsqueeze(2)
attn_metadata_i.decode.sin = builder.sin_cache[
positions].unsqueeze(1).unsqueeze(2)
attn_metadata_i.decode.actual_seq_lengths_q = attn_metadata_i.query_start_loc[
1:batch_size + 1].tolist()
attn_metadata_i.decode.cos = builder.cos_cache[
positions].unsqueeze(1).unsqueeze(2)
attn_metadata_i.decode.sin = builder.sin_cache[
positions].unsqueeze(1).unsqueeze(2)
# 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
@@ -601,61 +590,6 @@ class MtpProposer(Proposer):
draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
return draft_token_ids
def _get_torchair_lazy_compiled_model(self, batch_size: int):
if batch_size < 0 or batch_size > self.runner.torchair_graph_batch_sizes[
-1]:
raise ValueError(
f"Bad graph batch size:{batch_size}! max_graph_batch_sizes:{self.runner.torchair_graph_batch_sizes[-1]}"
)
compiled_model = self.torchair_compiled_models.get(
batch_size
) if self.runner.use_cached_npu_graph else self.torchair_compiled_model
if compiled_model:
return compiled_model
patch_for_hcom()
config = torchair.CompilerConfig()
config.experimental_config.frozen_parameter = True
config.experimental_config.tiling_schedule_optimize = True
config.experimental_config.enable_view_optimize = \
get_ascend_config().torchair_graph_config.enable_view_optimize
torch.npu.set_compile_mode(jit_compile=False)
if not self.runner.use_cached_npu_graph:
npu_backend = torchair.get_npu_backend(compiler_config=config)
self.torchair_compiled_model = torch.compile(
self.model,
dynamic=not self.use_sparse,
fullgraph=True,
backend=npu_backend)
return self.torchair_compiled_model
else:
# Generate a new forward proxy code object to prevent the invalidation of
# compilation cache caused by dynamo retracing
forward_proxy_name = f"{self.model.__class__.__name__}_forward_with_batch_size_{batch_size}"
forward_fn = self.model.forward
code = forward_fn.__code__
# Mark code object with a new proxy name
modified_code = code.replace(co_name=forward_proxy_name, )
modified_func = types.FunctionType(modified_code,
forward_fn.__globals__,
name=forward_proxy_name,
argdefs=forward_fn.__defaults__)
self.model.__dict__[forward_proxy_name] = modified_func.__get__(
self.model, nn.Module)
self.torchair_compiled_models[
batch_size] = torchair.inference.cache_compile(
self.model.__dict__[forward_proxy_name],
dynamic=not self.use_sparse,
fullgraph=True,
cache_dir=TORCHAIR_CACHE_DIR,
config=config,
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,
@@ -676,3 +610,160 @@ class MtpProposer(Proposer):
global_indices_flat = global_indices[mask]
values_flat = values[mask]
out_ptr[global_indices_flat] = values_flat
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
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 = AscendCommonAttentionMetadata(
query_start_loc=common_attn_metadata.query_start_loc,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens_cpu=common_attn_metadata.seq_lens,
num_reqs=common_attn_metadata.num_reqs,
num_actual_tokens=total_num_tokens,
max_query_len=new_query_len_per_req.max().item(),
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
block_table_tensor=common_attn_metadata.block_table_tensor,
slot_mapping=common_attn_metadata.slot_mapping,
positions=common_attn_metadata.positions,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
graph_pad_size=self.runner.graph_pad_size,
decode_token_per_req=self.runner.decode_token_per_req,
num_computed_tokens_cpu=common_attn_metadata.
num_computed_tokens_cpu,
seq_lens=common_attn_metadata.seq_lens)
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