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[Refactor] [SP]The sequence parallelism characteristics in the MoE and Dense models are integrated into a single solution. (#3085)

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What this PR does / why we need it?

there are two sets of sp implementations for moe and dense models. One
is called sequence_parallelism, and the other is flashcomm_v1.
We did the following things：

Merge two sets of code with the same implementation into one.
Remove the implementation of sequence_parallelism, as this solution
cannot support aclgraph.
Does this PR introduce any user-facing change?

No

How was this patch tested?

e2e&ut

- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9

---------

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
This commit is contained in:
weijinqian0
2025-09-24 11:29:59 +08:00
committed by GitHub
parent e7618d9414
commit 6aa4253798
14 changed files with 90 additions and 215 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
vllm_ascend/ascend_forward_context.py
View File

@@ -11,6 +11,7 @@ from vllm.forward_context import (BatchDescriptor, get_forward_context,
set_forward_context)
import vllm_ascend.envs as envs_ascend
from vllm_ascend.utils import enable_sp
class FusedMoEState(Enum):
@@ -101,21 +102,19 @@ def set_ascend_forward_context(
# due to multiple warmups before actual capturing
forward_context.capturing = False
# set for flashcomm_v1, 1000 is the batchsize concurrency threshold for enabling the flashcomm_v1 feature.
# set for sequence parallelism, 1000 is the batch size concurrency threshold for enabling the flashcomm_v1 or sequence_parallelism feature.
# Currently, it is an empirical value. In normal scenarios, if the concurrency exceeds this threshold,
# the performance benefits can be maximized. Conversely, if the concurrency is below the threshold,
# the performance may degrade due to the switching of communication methods.
flashcomm_v1_enabled = envs_ascend.VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE and \
envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM and \
sp_enabled = enable_sp() and \
tp_world_size > 1 and \
num_tokens is not None and num_tokens > 1000
if flashcomm_v1_enabled:
if sp_enabled:
pad_size = (tp_world_size -
(num_tokens % tp_world_size)) % tp_world_size
forward_context.pad_size = pad_size
forward_context.flashcomm_v1_enabled = flashcomm_v1_enabled
forward_context.sp_enabled = sp_enabled
# set this for rope forward_oot using
forward_context.is_first_layer = True

4
vllm_ascend/attention/attention_v1.py
View File

@@ -163,7 +163,6 @@ class AscendMetadata:
# *************************** Other Properties *************************** #
enable_dbo_across_dp: bool = False
is_only_prefill: bool = False
class AscendAttentionMetadataBuilder:
@@ -236,8 +235,7 @@ class AscendAttentionMetadataBuilder:
slot_mapping=slot_mapping,
attn_mask=attn_mask,
attn_state=attn_state,
enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp,
is_only_prefill=common_attn_metadata.is_only_prefill)
enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp)
return attn_metadata
def build_for_graph_capture(

116
vllm_ascend/models/qwen3_moe.py
View File

@@ -17,14 +17,14 @@
# Adapted from vllm/model_executor/models/qwen3_moe.py
# This file is a part of the vllm-ascend project.
from typing import Optional, Union
from typing import Optional
import torch
from torch import nn
from transformers import PretrainedConfig
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, CompilationLevel, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
get_tp_group)
from vllm.forward_context import get_forward_context
@@ -45,11 +45,8 @@ from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
from vllm.model_executor.models.utils import (
PPMissingLayer, extract_layer_index,
make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
from vllm.sequence import IntermediateTensors
from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.ops.sequence_parallel import (MetadataForPadding,
init_metadata_for_sp)
class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
@@ -100,7 +97,6 @@ class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
self,
hidden_states,
attn_metadata=None,
_metadata_for_padding: Optional[MetadataForPadding] = None,
):
if attn_metadata is None:
attn_metadata = get_forward_context().attn_metadata
@@ -119,7 +115,6 @@ class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
top_k=self.top_k,
enable_force_load_balance=enable_force_load_balance,
shared_experts=None,
_metadata_for_padding=_metadata_for_padding,
)
return hidden_states
@@ -188,60 +183,6 @@ class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.enable_sequence_parallelism = (
vllm_config.compilation_config.pass_config.
enable_sequence_parallelism if vllm_config is not None else False)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
_metadata_for_padding: Optional[MetadataForPadding] = None,
) -> torch.Tensor:
# To prevent precision issues during the decoder phase when only prefilling enables SP
if not self.enable_sequence_parallelism:
self.self_attn.o_proj.reduce_results = True
else:
self.self_attn.o_proj.reduce_results = not _metadata_for_padding.not_dummy_and_is_prefill if _metadata_for_padding is not None else True
# Self Attention
if residual is None:
residual = hidden_states
if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
residual = _metadata_for_padding.padding_slice(residual)
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
)
if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
hidden_states = _metadata_for_padding.padding_aligned_reduce_scatter(
hidden_states)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
if not self.use_aclgraph:
hidden_states = self.mlp(
hidden_states, _metadata_for_padding=_metadata_for_padding)
else:
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
@support_torch_compile
class CustomQwen3MoeModel(Qwen3MoeModel):
@@ -277,45 +218,6 @@ class CustomQwen3MoeModel(Qwen3MoeModel):
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
_metadata_for_padding: Optional[MetadataForPadding] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states, residual = layer(
positions,
hidden_states,
residual,
_metadata_for_padding=_metadata_for_padding)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
hidden_states, _ = self.norm(hidden_states, residual)
if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
hidden_states)
return hidden_states
class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
@@ -340,7 +242,6 @@ class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism
# Set MoE hyperparameters
self.expert_weights: list[torch.Tensor] = []
@@ -361,16 +262,3 @@ class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
self.num_moe_layers = len(self.moe_layers)
self.num_expert_groups = 1
self.num_shared_experts = 0
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
_metadata_for_padding = init_metadata_for_sp(
input_ids, self.enable_sequence_parallelism)
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds, _metadata_for_padding)
return hidden_states

4
vllm_ascend/ops/common_fused_moe.py
View File

@@ -216,7 +216,9 @@ class AscendFusedMoE(FusedMoE):
forward_context = get_forward_context()
hidden_states, router_logits = forward_context.moe_comm_method.prepare(
hidden_states=hidden_states, router_logits=router_logits)
hidden_states=hidden_states,
router_logits=router_logits,
replace_allreduce=forward_context.sp_enabled)
# Matrix multiply.
final_hidden_states = self.quant_method.apply(

15
vllm_ascend/ops/fused_moe.py
View File

@@ -21,8 +21,7 @@ from typing import Any, Callable, Optional
import torch
import torch_npu
from vllm.config import get_current_vllm_config
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
get_tp_group)
from vllm.forward_context import get_forward_context
@@ -42,7 +41,6 @@ from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.ops.moe.moe_comm_method import setup_moe_comm_method
from vllm_ascend.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ,
get_all_reduce_merge_state,
get_rm_router_logits_state, is_310p,
@@ -360,8 +358,7 @@ class AscendFusedMoE(FusedMoE):
top_k: Optional[int] = None,
shared_experts: Optional[Any] = None,
gate=None,
replace_allreduce: bool = False,
_metadata_for_padding: Optional[MetadataForPadding] = None):
replace_allreduce: bool = False):
assert self.quant_method is not None
@@ -379,13 +376,7 @@ class AscendFusedMoE(FusedMoE):
# When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
shared_hidden_states = shared_experts(hidden_states)
enable_sp = _metadata_for_padding is not None and _metadata_for_padding.not_dummy_and_is_prefill
tp_size = get_tensor_model_parallel_world_size()
if enable_sp:
tp_rank = get_tensor_model_parallel_rank()
mc2_mask_sp = _metadata_for_padding.mc2_mask if _metadata_for_padding is not None else forward_context.mc2_mask
chunk_mc2_mask = torch.tensor_split(mc2_mask_sp, tp_size, dim=0)
mc2_mask = chunk_mc2_mask[tp_rank]
if forward_context.sp_enabled:
replace_allreduce = True
hidden_states, router_logits = forward_context.moe_comm_method.prepare(

83
vllm_ascend/ops/linear_op.py
View File

@@ -48,8 +48,9 @@ from vllm.distributed.parallel_state import get_tp_group
from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
get_otp_group)
from vllm_ascend.utils import (dense_optim_enable, matmul_allreduce_enable,
mlp_tp_enable, oproj_tp_enable)
from vllm_ascend.utils import (dense_optim_enable, enable_sp,
matmul_allreduce_enable, mlp_tp_enable,
oproj_tp_enable)
class CustomTensorParallelOp:
@@ -82,10 +83,17 @@ class CustomTensorParallelOp:
self.skip_bias_add = self.layer.skip_bias_add
self.return_bias = self.layer.return_bias
self.quant_method = self.layer.quant_method
self.prefix = self.layer.prefix
def apply_impl(self, input_):
raise NotImplementedError
# Replace layer.forward to customize the layer computation process.
def apply(self, input_):
raise NotImplementedError
output, output_bias = self.apply_impl(input_)
if not self.return_bias:
return output
return output, output_bias
class CustomColumnParallelOp(CustomTensorParallelOp):
@@ -113,6 +121,14 @@ class CustomRowParallelOp(CustomTensorParallelOp):
self.reduce_results = self.layer.reduce_results
self.input_size_per_partition = self.layer.input_size_per_partition
def apply(self, input_):
output, output_bias = self.apply_impl(input_)
if dense_optim_enable():
torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
if not self.return_bias:
return output
return output, output_bias
class MLPColumnParallelOp(CustomColumnParallelOp):
@@ -123,7 +139,7 @@ class MLPColumnParallelOp(CustomColumnParallelOp):
def comm_group(self):
return get_mlp_tp_group()
def apply(
def apply_impl(
self,
input_: torch.Tensor,
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
@@ -134,14 +150,12 @@ class MLPColumnParallelOp(CustomColumnParallelOp):
output = self.quant_method.apply(self.layer, input_parallel, bias)
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
class DenseOptimMergedColumnParallelOp(CustomColumnParallelOp):
class SequenceMergedColumnParallelOp(CustomColumnParallelOp):
def apply(
def apply_impl(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
"""Linear layer with column parallelism.
@@ -164,18 +178,16 @@ class DenseOptimMergedColumnParallelOp(CustomColumnParallelOp):
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
class DenseOptimQKVParallelOp(CustomColumnParallelOp):
class SequenceQKVParallelOp(CustomColumnParallelOp):
def __init__(self, layer, prefix):
super().__init__(layer)
self.prefix = prefix
def apply(
def apply_impl(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
"""Linear layer with column parallelism.
@@ -201,8 +213,6 @@ class DenseOptimQKVParallelOp(CustomColumnParallelOp):
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
@@ -215,7 +225,7 @@ class MLPRowParallelOp(CustomRowParallelOp):
def comm_group(self):
return get_mlp_tp_group()
def apply(
def apply_impl(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
if self.input_is_parallel:
@@ -234,8 +244,6 @@ class MLPRowParallelOp(CustomRowParallelOp):
output = self.comm_group.reduce_scatter(output_parallel, 0)
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
@@ -248,7 +256,7 @@ class OProjRowParallelOp(CustomRowParallelOp):
def comm_group(self):
return get_otp_group()
def apply(
def apply_impl(
self,
input_: torch.Tensor,
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
@@ -294,8 +302,6 @@ class OProjRowParallelOp(CustomRowParallelOp):
# Handle bias return based on configuration
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
def update_attrs(self):
@@ -311,7 +317,7 @@ class MatmulAllreduceRowParallelOp(CustomRowParallelOp):
super().__init__(layer)
self.hcomm_info = self.get_hcomm_info(self.comm_group.device_group)
def apply(
def apply_impl(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
if self.input_is_parallel:
@@ -335,8 +341,6 @@ class MatmulAllreduceRowParallelOp(CustomRowParallelOp):
bias=bias_)
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
@classmethod
@@ -359,13 +363,13 @@ class MatmulAllreduceRowParallelOp(CustomRowParallelOp):
self.weight_t = self.layer.weight.t()
class DenseOptimRowParallelOp(CustomRowParallelOp):
class SequenceRowParallelOp(CustomRowParallelOp):
def __init__(self, layer, prefix):
super().__init__(layer)
self.prefix = prefix
def apply(
def apply_impl(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
"""Linear layer with column parallelism.
@@ -391,12 +395,8 @@ class DenseOptimRowParallelOp(CustomRowParallelOp):
input_parallel,
bias=bias_)
output = torch.ops.vllm.maybe_pad_and_reduce(output_parallel)
torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
def update_attrs(self):
@@ -407,23 +407,22 @@ class DenseOptimRowParallelOp(CustomRowParallelOp):
def get_column_parallel_op(
disable_tp, prefix, layer
) -> Tuple[
Optional[Union[MLPColumnParallelOp, DenseOptimMergedColumnParallelOp,
DenseOptimQKVParallelOp]], int, int]:
) -> Tuple[Optional[Union[MLPColumnParallelOp, SequenceMergedColumnParallelOp,
SequenceQKVParallelOp]], int, int]:
if disable_tp:
return None, 0, 1
custom_op: Optional[Union[
MLPColumnParallelOp,
DenseOptimMergedColumnParallelOp,
DenseOptimQKVParallelOp,
SequenceMergedColumnParallelOp,
SequenceQKVParallelOp,
]] = None
if "gate_up_proj" in prefix and mlp_tp_enable():
custom_op = MLPColumnParallelOp(layer)
elif "gate_up_proj" in prefix and dense_optim_enable():
custom_op = DenseOptimMergedColumnParallelOp(layer)
elif dense_optim_enable():
custom_op = DenseOptimQKVParallelOp(layer, prefix)
elif "gate_up_proj" in prefix and enable_sp():
custom_op = SequenceMergedColumnParallelOp(layer)
elif enable_sp():
custom_op = SequenceQKVParallelOp(layer, prefix)
if custom_op is not None:
return custom_op, custom_op.tp_rank, custom_op.tp_size
@@ -435,21 +434,21 @@ def get_row_parallel_op(
disable_tp, prefix, layer
) -> Tuple[Optional[Union[MLPRowParallelOp, OProjRowParallelOp,
MatmulAllreduceRowParallelOp,
DenseOptimRowParallelOp]], int, int]:
SequenceRowParallelOp]], int, int]:
if disable_tp:
return None, 0, 1
custom_op: Optional[Union[MLPRowParallelOp, OProjRowParallelOp,
MatmulAllreduceRowParallelOp,
DenseOptimRowParallelOp]] = None
SequenceRowParallelOp]] = None
if "down_proj" in prefix and mlp_tp_enable():
custom_op = MLPRowParallelOp(layer)
elif "o_proj" in prefix and oproj_tp_enable():
custom_op = OProjRowParallelOp(layer)
elif matmul_allreduce_enable():
custom_op = MatmulAllreduceRowParallelOp(layer)
elif dense_optim_enable():
custom_op = DenseOptimRowParallelOp(layer, prefix)
elif enable_sp():
custom_op = SequenceRowParallelOp(layer, prefix)
if custom_op is not None:
return custom_op, custom_op.tp_rank, custom_op.tp_size

35
vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py
View File

@@ -133,11 +133,15 @@ class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
"""
self.replace_allreduce = replace_allreduce
self.enable_shared_expert_dp = enable_shared_expert_dp
forward_context = get_forward_context()
mc2_mask = forward_context.mc2_mask
if self.tp_size > 1:
# Also slice mc2_mask
split_mc2_mask = torch.tensor_split(mc2_mask, self.tp_size, dim=0)
mc2_mask = split_mc2_mask[self.tp_rank]
if not self.replace_allreduce:
self.num_tokens, _ = hidden_states.shape
forward_context = get_forward_context()
mc2_mask = forward_context.mc2_mask
target_pad_length = forward_context.padded_num_tokens
pad_size = target_pad_length - self.num_tokens
@@ -149,23 +153,16 @@ class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
(0, 0, 0, pad_size))
# Slice across TP ranks
if self.tp_size > 1:
if not self.enable_shared_expert_dp:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
self.split_hidden_states = split_hidden_states # Save for finalize
# Also slice mc2_mask
split_mc2_mask = torch.tensor_split(mc2_mask,
self.tp_size,
dim=0)
mc2_mask = split_mc2_mask[self.tp_rank]
if self.tp_size > 1 and not self.enable_shared_expert_dp:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
self.split_hidden_states = split_hidden_states # Save for finalize
return hidden_states, router_logits, mc2_mask

15
vllm_ascend/ops/register_custom_ops.py
View File

@@ -20,10 +20,9 @@ def _maybe_chunk_residual_impl(x: torch.Tensor,
return residual
if x.size(0) != residual.size(0):
flashcomm_v1_enabled = forward_context.flashcomm_v1_enabled
assert flashcomm_v1_enabled is True, (
"Currently, this situation only occurs "
"when flashcomm_v1 is enabled")
sp_enabled = forward_context.sp_enabled
assert sp_enabled is True, ("Currently, this situation only occurs "
"when sp is enabled")
pad_size = forward_context.pad_size
if pad_size > 0:
residual = F.pad(residual, (0, 0, 0, pad_size))
@@ -41,8 +40,8 @@ def _maybe_all_gather_and_maybe_unpad_impl(x: torch.Tensor,
except AssertionError:
return x
flashcomm_v1_enabled = forward_context.flashcomm_v1_enabled
if flashcomm_v1_enabled and label:
sp_enabled = forward_context.sp_enabled
if sp_enabled and label:
x = tensor_model_parallel_all_gather(x, 0)
pad_size = forward_context.pad_size
if pad_size > 0:
@@ -56,8 +55,8 @@ def _maybe_pad_and_reduce_impl(x: torch.Tensor) -> torch.Tensor:
except AssertionError:
return tensor_model_parallel_all_reduce(x)
flashcomm_v1_enabled = forward_context.flashcomm_v1_enabled
if flashcomm_v1_enabled:
sp_enabled = forward_context.sp_enabled
if sp_enabled:
pad_size = forward_context.pad_size
if pad_size > 0:
x = F.pad(x, (0, 0, 0, pad_size))

6
vllm_ascend/platform.py
View File

@@ -282,12 +282,6 @@ class NPUPlatform(Platform):
ascend_config.ascend_scheduler_config)
vllm_config.scheduler_config = ascend_scheduler_config
if compilation_config.pass_config.enable_sequence_parallelism:
if not parallel_config.enable_expert_parallel or vllm_config.model_config.hf_config.model_type != "qwen3_moe":
raise NotImplementedError(
"For better performance in Qwen3 MoE, SP only works exclusively with MC2, AllToAll, and AllToAllV."
)
@classmethod
def get_attn_backend_cls(cls,
selected_backend,

4
vllm_ascend/torchair/models/qwen3_moe.py
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@@ -54,8 +54,8 @@ from vllm.sequence import IntermediateTensors
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.ops.sequence_parallel import (MetadataForPadding,
init_metadata_for_sp)
from vllm_ascend.torchair.ops.sequence_parallel import (MetadataForPadding,
init_metadata_for_sp)
class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):

0
vllm_ascend/ops/sequence_parallel.py → vllm_ascend/torchair/ops/sequence_parallel.py
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2
vllm_ascend/torchair/ops/torchair_fused_moe.py
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@@ -44,8 +44,8 @@ from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
determine_default_log2phy_map)
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.quantization.quant_config import AscendFusedMoEMethod
from vllm_ascend.torchair.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
from vllm_ascend.utils import (AscendSocVersion, dispose_tensor,
get_all_reduce_merge_state,

8
vllm_ascend/utils.py
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@@ -590,6 +590,14 @@ def dense_optim_enable() -> bool:
return envs_ascend.VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE
def enable_sp() -> bool:
from vllm.config import get_cached_compilation_config
return (
get_cached_compilation_config().pass_config.enable_sequence_parallelism
or envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM)
def is_moe_model(vllm_config: VllmConfig):
config = vllm_config.model_config.hf_config
return any('experts' in key.lower() for key in config.to_dict())

2
vllm_ascend/worker/model_runner_v1.py
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@@ -1582,7 +1582,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
update_attn_params(self.update_stream, forward_context,
positions.shape[0])
if get_forward_context().flashcomm_v1_enabled:
if get_forward_context().sp_enabled:
hidden_states = tensor_model_parallel_all_gather(hidden_states, 0)
pad_size = get_forward_context().pad_size
if pad_size > 0: