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[2/N][Feat] Attention and MoE weight prefetch in Qwen3MoE models (#3203)

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### What this PR does / why we need it?

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `gate_up_proj.weight` in quantized Attention modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency

### Does this PR introduce _any_ user-facing change?

Add a new config in `--additional-config` for configuration:
```json
{
    "weight_prefetch_config": {
        "enabled": True,
        "prefetch_ratio": {
            "moe": {
                "gate_up": 0.8
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: yuzhup <15705211260@163.com>
This commit is contained in:
yuzhup
2025-10-14 20:16:33 +08:00
committed by GitHub
parent 07e39620ea
commit 78777237a9
9 changed files with 160 additions and 100 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
docs/source/user_guide/configuration/additional_config.md
View File

@@ -73,10 +73,10 @@ ascend_scheduler_config also support the options from [vllm scheduler config](ht
**weight_prefetch_config**
| Name | Type | Default | Description |
|------------------|------|------------------------------------|------------------------------------|
| `enabled` | bool | `False` | Whether to enable weight prefetch. |
| `prefetch_ratio` | dict | `{"attn": {"qkv": 1.0, "o": 1.0}}` | Prefetch ratio of each weights. |
| Name | Type | Default | Description |
|------------------|------|-------------------------------------------------------------|------------------------------------|
| `enabled` | bool | `False` | Whether to enable weight prefetch. |
| `prefetch_ratio` | dict | `{"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}}` | Prefetch ratio of each weights. |
### Example
@@ -104,6 +104,9 @@ An example of additional configuration is as follows:
"qkv": 1.0,
"o": 1.0,
},
"moe": {
"gate_up": 0.8
}
},
},
"multistream_overlap_shared_expert": True,

8
tests/e2e/singlecard/ops/test_fused_moe.py
View File

@@ -291,7 +291,9 @@ def test_select_experts(
custom_routing_function.return_value = (mock_weights, mock_ids)
with patch("vllm_ascend.ops.moe.experts_selector._native_grouped_topk"
) as mock_native_grouped_topk:
) as mock_native_grouped_topk, \
patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
return_value=MagicMock(weight_prefetch_method=MagicMock())):
mock_native_grouped_topk.side_effect = lambda x, num_groups, k: torch.randn_like(
x)
@@ -325,7 +327,9 @@ def test_select_experts(
@pytest.mark.parametrize("device", DEVICE)
def test_select_experts_invalid_scoring_func(device: str):
with pytest.raises(ValueError,
with patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
return_value=MagicMock(weight_prefetch_method=MagicMock())), \
pytest.raises(ValueError,
match="Unsupported scoring function: invalid"):
select_experts(hidden_states=torch.randn(1, 128, device=device),
router_logits=torch.randn(1, 8, device=device),

22
tests/ut/ops/test_fused_ops.py
View File

@@ -92,14 +92,16 @@ def mock_dist_env(mocker: MockerFixture):
mock_moe_comm_method.finalize.side_effect = mock_finalize
dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
mock_forward_context_obj = MagicMock(moe_comm_method=mock_moe_comm_method,
moe_comm_type=MoECommType.MC2,
max_tokens_across_dp=10,
dp_metadata=dp_metadata,
mc2_mask=torch.zeros(
16, dtype=torch.bool),
padded_num_tokens=16,
with_quant=False)
mock_weight_prefetch_method = MagicMock()
mock_forward_context_obj = MagicMock(
moe_comm_method=mock_moe_comm_method,
moe_comm_type=MoECommType.MC2,
max_tokens_across_dp=10,
dp_metadata=dp_metadata,
mc2_mask=torch.zeros(16, dtype=torch.bool),
padded_num_tokens=16,
with_quant=False,
weight_prefetch_method=mock_weight_prefetch_method)
with patch('torch.distributed.get_rank', return_value=0), \
patch('torch.distributed.get_world_size', return_value=4), \
@@ -132,7 +134,9 @@ def mock_dist_env(mocker: MockerFixture):
patch('vllm_ascend.ops.moe.moe_comm_method.AlltoAllCommImpl._get_token_dispatcher',
return_value=None), \
patch('vllm_ascend.ops.moe.moe_comm_method.AllGatherCommImpl._get_token_dispatcher',
return_value=None):
return_value=None), \
patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
return_value=mock_forward_context_obj):
yield {
'mock_forward_context_obj': mock_forward_context_obj,

8
tests/ut/quantization/test_w8a8.py
View File

@@ -755,6 +755,14 @@ class TestSelectExperts(TestBase):
self.hidden_states = torch.randn(self.num_tokens, self.hidden_size)
self.router_logits = torch.randn(self.num_tokens, self.num_experts)
self.mock_ctx = MagicMock()
self.mock_ctx.weight_prefetch_method = MagicMock()
patcher = patch(
'vllm_ascend.ops.moe.experts_selector.get_forward_context',
return_value=self.mock_ctx)
self.addCleanup(patcher.stop)
patcher.start()
@patch('torch_npu.npu_moe_gating_top_k_softmax')
def test_softmax_scoring(self, mock_topk):
"""Test softmax scoring function"""

3
vllm_ascend/ascend_config.py
View File

@@ -216,6 +216,9 @@ class WeightPrefetchConfig:
"qkv": 1.0,
"o": 1.0,
},
"moe": {
"gate_up": 0.8
}
}
def __init__(self, weight_prefetch_config: dict):

2
vllm_ascend/ascend_forward_context.py
View File

@@ -145,7 +145,7 @@ def set_ascend_forward_context(
forward_context.prefetch_mlp_gate_up_proj = False
forward_context.prefetch_mlp_down_proj = False
forward_context.prefetch_mlp_enabled = prefetch_mlp_enabled
# TODO(yuzhup): integrate moe weight prefetch method
forward_context.model_instance = model_instance
forward_context.weight_prefetch_method = weight_prefetch_method
# TODO(rjg-lyh): The current implementation is somewhat brute force and not elegant.

7
vllm_ascend/ops/moe/experts_selector.py
View File

@@ -18,6 +18,7 @@ from typing import Callable, Optional
import torch
import torch_npu
from vllm.forward_context import get_forward_context
def return_row_idx(hidden_states, top_k):
@@ -65,7 +66,11 @@ def select_experts(hidden_states: torch.Tensor,
topk_weights: router weights of shape (num_tokens, top_k).
topk_ids: selected expert IDs of shape (num_tokens, top_k).
"""
# prefetch w1_w3_proj.weight preprocess
weight_prefetch_method = get_forward_context().weight_prefetch_method
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
hidden_states, "gate_up")
topk_weights, topk_ids, row_idx = _select_experts_with_fusion_ops(
hidden_states=hidden_states,
router_logits=router_logits,

4
vllm_ascend/ops/moe/moe_mlp.py
View File

@@ -78,6 +78,10 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
bias1, bias2 = None, None
_output_dtype = w2_scale.dtype
weight_prefetch_method = get_forward_context().weight_prefetch_method
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_moe_weight_postprocess(
hidden_states)
is_mc2 = get_forward_context().moe_comm_type == MoECommType.MC2
if w1_scale_bias is None and is_mc2:
if fusion and not dynamic_eplb:

195
vllm_ascend/ops/weight_prefetch.py
View File

@@ -1,83 +1,112 @@
from dataclasses import dataclass, field
import torch
import torch_npu
from vllm_ascend.ascend_config import WeightPrefetchConfig
from vllm_ascend.ops.linear import (AscendQKVParallelLinear,
AscendRowParallelLinear)
SUPPORTED_MODULES = ["attn", "mlp", "moe"]
@dataclass
class ModuleWeightPrefetchConfig:
module_name: str
enable: bool = False
prefetch_ratio: dict = field(default_factory=dict)
linear_prefix_map: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.prefetch_ratio = {
prefix: ratio
for prefix, ratio in self.prefetch_ratio.items() if 0 <= ratio <= 1
}
assert self.module_name in SUPPORTED_MODULES, (
f"Invalid module name {self.module_name}, should be one of {SUPPORTED_MODULES}"
)
if self.module_name in SUPPORTED_MODULES:
self.enable = self.enable and any(self.prefetch_ratio.values()) > 0
class WeightPrefetchMethod:
"""
Unified weight prefetch method.
"""
def __init__(self, weight_prefetch_config: WeightPrefetchConfig) -> None:
self.attn = ModuleWeightPrefetchConfig(
module_name="attn",
enable=weight_prefetch_config.enabled,
prefetch_ratio=weight_prefetch_config.prefetch_ratio.get(
"attn", {}),
linear_prefix_map={
AscendQKVParallelLinear.__name__: "qkv",
AscendRowParallelLinear.__name__: "o",
})
def maybe_prefetch_attn_weight_preprocess(
self, layer_cls_name: str, weight: torch.Tensor,
start_flag: torch.Tensor) -> None:
if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
return
prefix = self.attn.linear_prefix_map.get(layer_cls_name, "")
weight_size = weight.data.element_size() * weight.data.numel(
) * self.attn.prefetch_ratio.get(prefix, 0)
torch.ops.vllm.prefetch_preprocess(weight=weight,
start_flag=start_flag,
max_weight_size=int(weight_size))
def maybe_prefetch_attn_weight_postprocess(
self, layer_cls_name: str, stop_flag: torch.Tensor) -> None:
if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
return
torch.ops.vllm.prefetch_postprocess(stop_flag)
def maybe_npu_prefetch(inputs: torch.Tensor,
dependency: torch.Tensor,
max_size: int = 0,
offset: int = 0,
*,
enabled: bool = True) -> None:
if not enabled:
return
input_size = inputs.element_size() * inputs.numel()
if max_size <= 0 or max_size > input_size:
max_size = input_size
torch_npu.npu_prefetch(inputs, dependency, max_size, offset)
from dataclasses import dataclass, field
import torch
import torch_npu
from vllm.forward_context import get_forward_context
from vllm_ascend.ascend_config import WeightPrefetchConfig
from vllm_ascend.ops.linear import (AscendQKVParallelLinear,
AscendRowParallelLinear)
SUPPORTED_MODULES = ["attn", "mlp", "moe"]
MOE_PREFETCH_TOKEN_THRESHOLD = 96
@dataclass
class ModuleWeightPrefetchConfig:
module_name: str
enable: bool = False
is_active_this_forward: bool = False
prefetch_ratio: dict = field(default_factory=dict)
linear_prefix_map: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.prefetch_ratio = {
prefix: ratio
for prefix, ratio in self.prefetch_ratio.items() if 0 <= ratio <= 1
}
assert self.module_name in SUPPORTED_MODULES, (
f"Invalid module name {self.module_name}, should be one of {SUPPORTED_MODULES}"
)
if self.module_name in SUPPORTED_MODULES:
self.enable = self.enable and any(self.prefetch_ratio.values()) > 0
class WeightPrefetchMethod:
"""
Unified weight prefetch method.
"""
def __init__(self, weight_prefetch_config: WeightPrefetchConfig) -> None:
self.attn = ModuleWeightPrefetchConfig(
module_name="attn",
enable=weight_prefetch_config.enabled,
prefetch_ratio=weight_prefetch_config.prefetch_ratio.get(
"attn", {}),
linear_prefix_map={
AscendQKVParallelLinear.__name__: "qkv",
AscendRowParallelLinear.__name__: "o",
})
self.moe = ModuleWeightPrefetchConfig(
module_name="moe",
enable=weight_prefetch_config.enabled,
prefetch_ratio=weight_prefetch_config.prefetch_ratio.get(
"moe", {}))
def maybe_prefetch_attn_weight_preprocess(
self, layer_cls_name: str, weight: torch.Tensor,
start_flag: torch.Tensor) -> None:
if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
return
prefix = self.attn.linear_prefix_map.get(layer_cls_name, "")
weight_size = weight.data.element_size() * weight.data.numel(
) * self.attn.prefetch_ratio.get(prefix, 0)
torch.ops.vllm.prefetch_preprocess(weight=weight,
start_flag=start_flag,
max_weight_size=int(weight_size))
def maybe_prefetch_attn_weight_postprocess(
self, layer_cls_name: str, stop_flag: torch.Tensor) -> None:
if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
return
torch.ops.vllm.prefetch_postprocess(stop_flag)
def maybe_prefetch_moe_weight_preprocess(self, hidden_states, prefix):
self.moe.is_active_this_forward = hidden_states.shape[
0] >= MOE_PREFETCH_TOKEN_THRESHOLD if self.moe.enable else False
if not self.moe.is_active_this_forward:
return
forward_context = get_forward_context()
weight = forward_context.model_instance.model.layers[
forward_context.layer_idx].mlp.experts.w13_weight
weight_size = weight.data.element_size() * weight.data.numel(
) * self.moe.prefetch_ratio.get(prefix, 0)
torch.ops.vllm.prefetch_preprocess(weight=weight,
start_flag=None,
max_weight_size=int(weight_size))
forward_context.layer_idx += 1
def maybe_prefetch_moe_weight_postprocess(self, stop_flag: torch.Tensor):
if not self.moe.is_active_this_forward:
return
torch.ops.vllm.prefetch_postprocess(stop_flag)
def maybe_npu_prefetch(inputs: torch.Tensor,
dependency: torch.Tensor,
max_size: int = 0,
offset: int = 0,
*,
enabled: bool = True) -> None:
if not enabled:
return
input_size = inputs.element_size() * inputs.numel()
if max_size <= 0 or max_size > input_size:
max_size = input_size
torch_npu.npu_prefetch(inputs, dependency, max_size, offset)