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Support multistream of shared experts in FusedMoE (#997)

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Contains on #1111 for completeness.

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### What this PR does / why we need it?
Implement multi-stream parallelism for MoE layers with shared experts,
where computation of shared experts will be overlapped with expert token
dispatch and combine. Also, when multi-stream is enabled, weights of
shared experts will be force to replicate across all cards, regardless
of any tensor parallelism configurations, to avoid AllReduce operations.

With the expected overlaping being:
```
| shared gate_up | shared act |              | shared down |
|    dispatch    | routed gate_up, act, down |   combine   |
```

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### Does this PR introduce _any_ user-facing change?
No.

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### How was this patch tested?
Tested on 1x16 910 node, with tailored 2 layer DSKv2.
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---------

Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
This commit is contained in:
sdmyzlp
2025-06-11 09:18:38 +08:00
committed by GitHub
parent 04abfd8721
commit 7bdc606677
11 changed files with 296 additions and 308 deletions
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110
vllm_ascend/models/deepseek_dbo.py
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@@ -29,7 +29,7 @@ from typing import Any, Dict, List, Optional, Union
import torch
import torch.distributed as dist
import torch_npu
import torch_npu # noqa: F401
import vllm.envs as envs
from torch import nn
from transformers import PretrainedConfig
@@ -40,13 +40,10 @@ from vllm.distributed import (get_pp_group,
get_tp_group, tensor_model_parallel_all_reduce)
from vllm.distributed.parallel_state import get_dp_group
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear,
UnquantizedLinearMethod)
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
@@ -67,6 +64,7 @@ from vllm.sequence import IntermediateTensors
import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.models.deepseek_v2 import CustomDeepseekV2MLP
from vllm_ascend.multistream.base import MSEventKey
from vllm_ascend.multistream.context import (
advance_step_multistream_layer_context, get_multistream_comm_context,
@@ -78,117 +76,17 @@ from vllm_ascend.multistream.metadata import (MultiStreamConfig,
make_multistream_metadata_ds)
from vllm_ascend.multistream.ms_split import compute_split_seq_index
from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
from vllm_ascend.utils import dispose_tensor
VLLM_ASCEND_ENABLE_DBO: bool = envs_ascend.VLLM_ASCEND_ENABLE_DBO
VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
class CustomDeepseekDBOMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
prefix: str = "",
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
quant_config=quant_config,
reduce_results=reduce_results,
prefix=f"{prefix}.down_proj")
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
# NOTE: `torch_npu.npu_dequant_swiglu_quant` can only be enabled in dynamic quant
self.is_dynamic_quant = not isinstance(
self.gate_up_proj.quant_method,
UnquantizedLinearMethod) and isinstance(
self.gate_up_proj.quant_method.quant_method,
AscendW8A8DynamicLinearMethod)
def forward(self, x):
if self.is_dynamic_quant:
x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
x = torch_npu.npu_quant_matmul(
x,
self.gate_up_proj.weight,
self.gate_up_proj.weight_scale,
output_dtype=torch.int32,
)
x, dynamic_scale = torch_npu.npu_dequant_swiglu_quant(
x=x,
weight_scale=self.gate_up_proj.weight_scale_fp32,
activation_scale=dynamic_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=None,
activate_left=True,
quant_mode=1)
x = torch_npu.npu_quant_matmul(
x,
self.down_proj.weight,
self.down_proj.weight_scale,
pertoken_scale=dynamic_scale,
output_dtype=torch.bfloat16,
)
if self.down_proj.reduce_results and self.down_proj.tp_size > 1:
x = tensor_model_parallel_all_reduce(x)
return x
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class CustomDeepseekDBOMLP(CustomDeepseekV2MLP):
def _forward_ms_mlp(self, x):
current_ms_metadata = get_multistream_comm_context()
assert current_ms_metadata is not None
if self.is_dynamic_quant:
x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
x = torch_npu.npu_quant_matmul(
x,
self.gate_up_proj.weight,
self.gate_up_proj.weight_scale,
output_dtype=torch.int32,
)
x, dynamic_scale = torch_npu.npu_dequant_swiglu_quant(
x=x,
weight_scale=self.gate_up_proj.weight_scale_fp32,
activation_scale=dynamic_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=None,
activate_left=True,
quant_mode=1)
x = torch_npu.npu_quant_matmul(
x,
self.down_proj.weight,
self.down_proj.weight_scale,
pertoken_scale=dynamic_scale,
output_dtype=torch.bfloat16,
)
if self.down_proj.reduce_results and self.down_proj.tp_size > 1:
current_ms_metadata.before_comm_event.record()
with torch.npu.stream(current_ms_metadata.comm_stream):
current_ms_metadata.before_comm_event.wait()
x = tensor_model_parallel_all_reduce(x)
current_ms_metadata.after_comm_event.record()
return x
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
current_ms_metadata.before_comm_event.record()