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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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142
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -15,19 +15,19 @@
# limitations under the License.
#
from typing import Any, Callable, Dict, Optional
from typing import Any, Callable, Dict, Optional, Tuple, Union
import torch
import torch.distributed as dist
import torch_npu
import torchair as tng # type: ignore
from vllm.distributed import GroupCoordinator, tensor_model_parallel_all_reduce
from vllm.distributed import GroupCoordinator
import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.parallel_state import get_ep_group
from vllm_ascend.ops.fused_moe import select_experts
from vllm_ascend.utils import dispose_tensor
from vllm_ascend.utils import (dispose_tensor, npu_stream_switch,
npu_wait_tensor)
VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
@@ -39,8 +39,7 @@ def apply_mlp(hidden_states: torch.Tensor,
w2_scale: torch.Tensor,
group_list: torch.Tensor,
dynamic_scale: torch.Tensor = None,
group_list_type: int = 1,
**kwargs) -> torch.Tensor:
group_list_type: int = 1) -> torch.Tensor:
"""
apply MLP: gate_up_proj -> swiglu -> down_proj
@@ -74,23 +73,6 @@ def apply_mlp(hidden_states: torch.Tensor,
else:
pertoken_scale = dynamic_scale
shared_experts = kwargs.get('shared_experts', None)
if shared_experts:
shared_gate_up = kwargs.get('shared_gate_up', None)
shared_dynamic_scale = kwargs.get('shared_dynamic_scale', None)
with tng.scope.npu_stream_switch('cv'):
tng.scope.npu_wait_tensor(shared_gate_up, hidden_states)
shared_x, shared_dynamic_scale = torch_npu.npu_dequant_swiglu_quant(
x=shared_gate_up,
weight_scale=shared_experts.gate_up_proj.weight_scale_fp32,
activation_scale=shared_dynamic_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=None,
activate_left=True,
quant_mode=1)
# gmm1: gate_up_proj
hidden_states = torch_npu.npu_grouped_matmul(
x=[hidden_states],
@@ -120,36 +102,24 @@ def apply_mlp(hidden_states: torch.Tensor,
group_list=group_list,
output_dtype=w2_scale.dtype)[0]
if shared_experts:
with tng.scope.npu_stream_switch('cv'):
tng.scope.npu_wait_tensor(shared_x, hidden_states)
shared_output = torch_npu.npu_quant_matmul(
shared_x,
shared_experts.down_proj.weight,
shared_experts.down_proj.weight_scale,
pertoken_scale=shared_dynamic_scale,
output_dtype=torch.bfloat16,
)
if shared_experts.down_proj.reduce_results and shared_experts.down_proj.tp_size > 1:
shared_output = tensor_model_parallel_all_reduce(shared_output)
if shared_experts:
return hidden_states, shared_output
return hidden_states
def fused_experts_with_mc2(hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
expert_map: torch.Tensor = None,
moe_all_to_all_group_name: str = "",
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
**kwargs) -> torch.Tensor:
def fused_experts_with_mc2(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
top_k: int,
expert_map: torch.Tensor = None,
moe_all_to_all_group_name: str = "",
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
shared_experts: Optional[Any] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
if log2phy:
topk_ids = log2phy[topk_ids]
global_bs = 0
@@ -188,31 +158,17 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
}
kwargs_mc2.update(stage1_kwargs)
shared_experts = kwargs.get('shared_experts', None)
if shared_experts:
shared_hidden_states = kwargs.get('shared_hidden_states', None)
with tng.scope.npu_stream_switch('cv'):
tng.scope.npu_wait_tensor(shared_hidden_states, hidden_states)
shared_x, shared_dynamic_scale = torch_npu.npu_dynamic_quant(
shared_hidden_states)
shared_gate_up = torch_npu.npu_quant_matmul(
shared_x,
shared_experts.gate_up_proj.weight,
shared_experts.gate_up_proj.weight_scale,
output_dtype=torch.int32,
)
kwargs.update({
"shared_gate_up": shared_gate_up,
"shared_dynamic_scale": shared_dynamic_scale,
})
output = torch_npu.npu_moe_distribute_dispatch(**kwargs_mc2)
# comm_stream.wait_stream(torch.npu.current_stream())
expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts = output[
0:5]
if quant_mode == 0:
dynamic_scale = None
if shared_experts is not None:
with npu_stream_switch("moe_secondary", 0):
npu_wait_tensor(hidden_states, topk_weights)
shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
npu_wait_tensor(shared_gate_up[0], expand_x)
shared_act = shared_experts.act_fn(shared_gate_up)
# `expand_x` will be disposed in the `apply_mlp` function
down_out_list = apply_mlp(expand_x,
@@ -221,12 +177,7 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
w2,
w2_scale,
expert_token_nums,
dynamic_scale=dynamic_scale,
**kwargs)
multi_stream = isinstance(down_out_list, tuple)
if multi_stream:
down_out_list, shared_output = down_out_list
dynamic_scale=dynamic_scale)
# moeCombine
kwargs_mc2 = {
@@ -257,9 +208,13 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2)
if multi_stream:
if shared_experts is None:
return hidden_states
else:
with npu_stream_switch("moe_secondary", 0):
npu_wait_tensor(shared_act[0], down_out_list)
shared_output, _ = shared_experts.down_proj(shared_act)
return hidden_states, shared_output
return hidden_states
# currently expert parallelism implemented with all2all
@@ -541,21 +496,33 @@ class AscendW8A8DynamicLinearMethod:
@staticmethod
def apply(
layer: torch.nn.Module,
x: torch.Tensor,
x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
bias: Optional[torch.Tensor] = None,
tp_rank: Optional[int] = 0,
) -> torch.Tensor:
original_dtype = x.dtype
# use ATB quantize
quant_out, dynamic_scale = torch_npu.npu_dynamic_quant(x)
return torch_npu.npu_quant_matmul(
quant_out,
config = getattr(layer, "_ascend_quant_config", {})
if not isinstance(x, tuple):
output_dtype = config.get("output_dtype", x.dtype)
quantized_x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
else:
assert "output_dtype" in config.keys(), (
f"DynamicLinearMethod needs explicitly specified `output_dtype`"
f"for pre-quantized input, got config [{config}]")
output_dtype = config["output_dtype"]
quantized_x, dynamic_scale = x
pertoken_scale = (dynamic_scale
if config.get("pertoken_scale", True) else None)
output = torch_npu.npu_quant_matmul(
quantized_x,
layer.weight,
layer.weight_scale,
pertoken_scale=dynamic_scale,
pertoken_scale=pertoken_scale,
bias=bias,
output_dtype=original_dtype,
output_dtype=output_dtype,
)
return ((output, dynamic_scale)
if config.get("return_scale", False) else output)
def process_weights_after_loading(self, layer):
if self.transpose_weight:
@@ -650,6 +617,7 @@ class AscendW8A8DynamicFusedMoEMethod:
enable_force_load_balance: bool = True,
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
shared_experts: Optional[Any] = None,
**kwargs,
) -> torch.Tensor:
assert router_logits.shape[
@@ -706,7 +674,7 @@ class AscendW8A8DynamicFusedMoEMethod:
moe_all_to_all_group_name=self.moe_all_to_all_group_name,
log2phy=log2phy,
global_redundant_expert_num=global_redundant_expert_num,
**kwargs)
shared_experts=shared_experts)
elif self.torchair_graph_enabled or self.ep_group.world_size == 1:
return fused_experts(hidden_states=x,
w1=layer.w13_weight,