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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #11) (#6176)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/ops/fused_moe/comm_utils.py` |
| `vllm_ascend/ops/fused_moe/experts_selector.py` |
| `vllm_ascend/ops/fused_moe/fused_moe.py` |
| `vllm_ascend/ops/fused_moe/moe_comm_method.py` |
| `vllm_ascend/ops/fused_moe/moe_mlp.py` |
| `vllm_ascend/ops/fused_moe/prepare_finalize.py` |
| `vllm_ascend/ops/fused_moe/token_dispatcher.py` |

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

### How was this patch tested?

- vLLM version: v0.14.0
- vLLM main:
d68209402d

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-02-06 15:28:49 +08:00
committed by GitHub
parent 4fb3d5e1b2
commit 65b7f716e6
8 changed files with 694 additions and 784 deletions
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162
vllm_ascend/ops/fused_moe/experts_selector.py
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@@ -14,26 +14,28 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Callable, Optional
from collections.abc import Callable
import torch
from vllm_ascend.utils import get_weight_prefetch_method
def select_experts(hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
use_grouped_topk: bool,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
routed_scaling_factor=1.0,
e_score_correction_bias: Optional[torch.Tensor] = None,
indices_type: Optional[torch.dtype] = None,
global_num_experts: int = -1):
def select_experts(
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
use_grouped_topk: bool,
renormalize: bool,
topk_group: int | None = None,
num_expert_group: int | None = None,
custom_routing_function: Callable | None = None,
scoring_func: str = "softmax",
routed_scaling_factor=1.0,
e_score_correction_bias: torch.Tensor | None = None,
indices_type: torch.dtype | None = None,
global_num_experts: int = -1,
):
"""
Fused experts with select experts.
@@ -58,8 +60,7 @@ def select_experts(hidden_states: torch.Tensor,
# prefetch w1_w3_proj.weight preprocess
weight_prefetch_method = get_weight_prefetch_method()
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
hidden_states, "gate_up")
weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
is_support_npu_moe_gating_top_k = check_npu_moe_gating_top_k(
hidden_states=hidden_states,
top_k=top_k,
@@ -67,7 +68,8 @@ def select_experts(hidden_states: torch.Tensor,
topk_group=topk_group,
num_expert_group=num_expert_group,
scoring_func=scoring_func,
custom_routing_function=custom_routing_function)
custom_routing_function=custom_routing_function,
)
if is_support_npu_moe_gating_top_k:
topk_weights, topk_ids = _select_experts_with_fusion_ops(
@@ -81,7 +83,8 @@ def select_experts(hidden_states: torch.Tensor,
num_expert_group=num_expert_group,
scoring_func=scoring_func,
routed_scaling_factor=routed_scaling_factor,
global_num_experts=global_num_experts)
global_num_experts=global_num_experts,
)
else:
topk_weights, topk_ids = _native_select_experts(
hidden_states=hidden_states,
@@ -100,14 +103,15 @@ def select_experts(hidden_states: torch.Tensor,
def check_npu_moe_gating_top_k(
hidden_states: torch.Tensor,
top_k: int,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
scoring_func: str = "softmax",
custom_routing_function: Optional[Callable] = None):
if scoring_func == "sigmoid" and not renormalize: #sigmoid + renorm=0 is not supported in current branch
hidden_states: torch.Tensor,
top_k: int,
renormalize: bool,
topk_group: int | None = None,
num_expert_group: int | None = None,
scoring_func: str = "softmax",
custom_routing_function: Callable | None = None,
):
if scoring_func == "sigmoid" and not renormalize: # sigmoid + renorm=0 is not supported in current branch
return False
if custom_routing_function is not None:
return False
@@ -115,39 +119,39 @@ def check_npu_moe_gating_top_k(
return False
topk_group = topk_group if topk_group is not None else 1
num_expert_group = num_expert_group if num_expert_group is not None else 1
if not (num_expert_group > 0 and hidden_states.shape[-1] % num_expert_group
== 0 and hidden_states.shape[-1] // num_expert_group > 2):
if not (
num_expert_group > 0
and hidden_states.shape[-1] % num_expert_group == 0
and hidden_states.shape[-1] // num_expert_group > 2
):
return False
if topk_group < 1 or topk_group > num_expert_group:
return False
if top_k < 1 or \
top_k > (hidden_states.shape[-1] / (num_expert_group * topk_group)):
if top_k < 1 or top_k > (hidden_states.shape[-1] / (num_expert_group * topk_group)):
return False
if topk_group * hidden_states.shape[-1] / num_expert_group < top_k:
if topk_group * hidden_states.shape[-1] / num_expert_group < top_k: # noqa: SIM103
return False
return True
def _native_grouped_topk(
topk_weights: torch.Tensor,
num_expert_group: Optional[int],
topk_group: Optional[int],
num_expert_group: int | None,
topk_group: int | None,
):
topk_group = 0 if topk_group is None else topk_group
num_expert_group = 0 if num_expert_group is None else num_expert_group
num_token = topk_weights.shape[0]
grouped_weights = topk_weights.view(num_token, num_expert_group,
-1).max(dim=-1).values
topk_group_indices = torch.topk(grouped_weights.to(torch.float32),
k=topk_group,
dim=-1,
sorted=False)[1]
grouped_weights = topk_weights.view(num_token, num_expert_group, -1).max(dim=-1).values
topk_group_indices = torch.topk(grouped_weights.to(torch.float32), k=topk_group, dim=-1, sorted=False)[1]
topk_group_mask = torch.zeros_like(grouped_weights)
topk_group_mask.scatter_(1, topk_group_indices, 1)
topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand(
num_token, num_expert_group,
topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1))
topk_weight_mask = (
topk_group_mask.unsqueeze(-1)
.expand(num_token, num_expert_group, topk_weights.shape[-1] // num_expert_group)
.reshape(num_token, -1)
)
topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)
return topk_weights
@@ -163,9 +167,13 @@ def _renormalize_topk_weights(
def _select_expert_use_group_topk(
topk_weights: torch.Tensor, topk_group: Optional[int],
renormalize: bool, top_k: int, num_expert_group: Optional[int],
e_score_correction_bias: Optional[torch.Tensor]):
topk_weights: torch.Tensor,
topk_group: int | None,
renormalize: bool,
top_k: int,
num_expert_group: int | None,
e_score_correction_bias: torch.Tensor | None,
):
assert topk_group is not None
assert num_expert_group is not None
@@ -177,47 +185,38 @@ def _select_expert_use_group_topk(
# TODO: Change to npu_group_topk when the latest CANN and NNAL is available
# >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
topk_weights = _native_grouped_topk(topk_weights, num_expert_group,
topk_group)
topk_weights = _native_grouped_topk(topk_weights, num_expert_group, topk_group)
# TODO bfloat16 is not supported in torch.topk with ge graph.
if e_score_correction_bias is not None:
topk_ids = torch.topk(topk_weights.to(torch.float32),
k=top_k,
dim=-1,
sorted=False)[1]
topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False)[1]
# Use original unbiased scores for the routing weights
topk_weights = original_weights.gather(1, topk_ids)
else:
topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32),
k=top_k,
dim=-1,
sorted=False)
topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False)
topk_ids = topk_ids.to(torch.int32)
topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
return topk_weights, topk_ids
def _select_experts_with_fusion_ops(
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
use_grouped_topk: bool,
renormalize: bool,
e_score_correction_bias: Optional[torch.Tensor],
topk_group: Optional[int],
num_expert_group: Optional[int],
scoring_func: str = "softmax",
routed_scaling_factor=1.0,
global_num_experts: int = -1):
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
use_grouped_topk: bool,
renormalize: bool,
e_score_correction_bias: torch.Tensor | None,
topk_group: int | None,
num_expert_group: int | None,
scoring_func: str = "softmax",
routed_scaling_factor=1.0,
global_num_experts: int = -1,
):
topk_group = topk_group if topk_group is not None else 1
num_expert_group = num_expert_group if num_expert_group is not None else 1
renorm = int(renormalize)
norm_type = 0 if scoring_func == "softmax" else 1
if e_score_correction_bias is not None and \
e_score_correction_bias.dtype != router_logits.dtype:
e_score_correction_bias = e_score_correction_bias.to(
router_logits.dtype)
if e_score_correction_bias is not None and e_score_correction_bias.dtype != router_logits.dtype:
e_score_correction_bias = e_score_correction_bias.to(router_logits.dtype)
topk_weights, topk_ids, _ = torch.ops._C_ascend.moe_gating_top_k(
router_logits,
k=top_k,
@@ -228,7 +227,7 @@ def _select_experts_with_fusion_ops(
norm_type=norm_type, # 0: softmax; 1: sigmoid
out_flag=False,
routed_scaling_factor=routed_scaling_factor,
eps=float(1e-20),
eps=1e-20,
bias_opt=e_score_correction_bias,
)
@@ -241,12 +240,12 @@ def _native_select_experts(
top_k: int,
use_grouped_topk: bool,
renormalize: bool,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
custom_routing_function: Optional[Callable] = None,
topk_group: int | None = None,
num_expert_group: int | None = None,
custom_routing_function: Callable | None = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
global_num_experts: Optional[torch.Tensor] = None
e_score_correction_bias: torch.Tensor | None = None,
global_num_experts: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Select top-k experts based on router logits.
@@ -285,7 +284,8 @@ def _native_select_experts(
renormalize=renormalize,
topk_group=topk_group,
num_expert_group=num_expert_group,
e_score_correction_bias=e_score_correction_bias)
e_score_correction_bias=e_score_correction_bias,
)
if custom_routing_function is not None:
topk_weights, topk_ids = custom_routing_function(
@@ -293,7 +293,8 @@ def _native_select_experts(
gating_output=router_logits,
topk=top_k,
renormalize=renormalize,
global_num_experts=global_num_experts)
global_num_experts=global_num_experts,
)
# Required by npu_moe_init_routing
topk_ids = topk_ids.to(torch.int32)
return topk_weights, topk_ids
@@ -318,8 +319,7 @@ def zero_experts_compute(
if zero_expert_type == "identity":
zero_expert_mask = expert_indices < num_experts
zero_expert_scales = expert_scales.clone()
zero_expert_scales = torch.where(zero_expert_mask, 0.0,
zero_expert_scales)
zero_expert_scales = torch.where(zero_expert_mask, 0.0, zero_expert_scales)
hidden_states = hidden_states.unsqueeze(1)
zero_expert_scales = zero_expert_scales.unsqueeze(2)